Data analytics for intelligent healthcare management academic press (2019)

Big Data Analytics for Intelligent Healthcare Management Advances in Ubiquitous Sensing Applications for Healthcare Big Data Analytics for Intelligent Healthcare Management Volume Three Series Editors Nilanjan Dey Amira S Ashour Simon James Fong Volume Editors Nilanjan Dey Techno India College of Technology, Rajarhat, India Himansu Das KIIT, Bhubaneswar, India Bighnaraj Naik VSSUT, Burla, India Himansu Sekhar Behera VSSUT, Burla, India Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom # 2019 Elsevier Inc All rights reserved No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing 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responsibility To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-818146-1 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals Publisher: Mara Conner Acquisition Editor: Chris Katsaropoulos Editorial Project Manager: Ana Claudia A Garcia Production Project Manager: Punithavathy Govindaradjane Cover Designer: Christian Bilbow Typeset by SPi Global, India Contributors Satyabrata Aich Department of Computer Engineering, Inje University, Gimhae, South Korea Navneet Arora Indian Institute of Technology, Roorkee, India Rabindra Kumar Barik KIIT, Bhubaneswar, India Akalabya Bissoyi Department of Biomedical Engineering, National Institute of Technology, Raipur, India Dibya Jyoti Bora School of Computing Sciences, Kaziranga University, Jorhat, India Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia D.K Chaturvedi Dayalbagh Educational Institute, Agra, India Sumit Chauhan ABES Engineering College, Ghaziabad, India Himansu Das KIIT, Bhubaneswar, India Satya Ranjan Dash School of Computer Applications, Kalinga Institute of Industrial Technology, Bhubaneswar, India Pandit Byomakesha Dash Department of Computer Application, Veer Surendra Sai University of Technology, Burla, India Sukhpal Singh Gill Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia Mayank Gupta Tata Consultancy Services, Noida, India Somnath Karmakar Government College of Engineering and Leather Technology, Kolkata, India Ramgopal Kashyap Amity School of Engineering & Technology, Amity University Chhattisgarh, Raipur, India Fuad Khan Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh Chul-Soo Kim Department of Computer Engineering, Inje University, Gimhae, South Korea xiii xiv Contributors Hee-Cheol Kim Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae, South Korea Pradeep Kumar Maharana Department of Physics, Silicon Institute of Technology, Bhubaneswar, India Sitikantha Mallik KIIT, Bhubaneswar, India Sushma Rani Martha Orissa University of Agriculture and Technology, Bhubaneswar, India Bhabani Shankar Prasad Mishra KIIT, Bhubaneswar, India Chinmaya Misra School of Computer Applications, Kalinga Institute of Industrial Technology, Bhubaneswar, India Suchismita Mohanty Department of Computer Science and Engineering, College of Engineering and Technology, Bhubaneswar, India Subhadarshini Mohanty Department of Computer Science and Engineering, College of Engineering and Technology, Bhubaneswar, India Subasish Mohapatra Department of Computer Science and Engineering, College of Engineering and Technology, Bhubaneswar, India Maheswata Moharana Department of Chemistry, Utkal University; Department of Hydrometallurgy, CSIR-Institute of Minerals and Material Technology, Bhubaneswar, India Bighnaraj Naik Department of Computer Application, Veer Surendra Sai University of Technology, Burla, India Janmenjoy Nayak Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, India Md Nuruddin Qaisar Bhuiyan Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh Md Mehedi Hassan Onik Department of Computer Engineering, Inje University, Gimhae, South Korea Luina Pani School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India Subrat Kumar Pattanayak Department of Chemistry, National Institute of Technology, Raipur, India Chittaranjan Pradhan KIIT, Bhubaneswar, India Contributors Farhin Haque Proma Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh Rohit Rastogi ABES Engineering College, Ghaziabad, India Shamim H Ripon Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh Abhaya Kumar Sahoo KIIT, Bhubaneswar, India Satya Narayan Sahu Orissa University of Agriculture and Technology, Bhubaneswar, India Santosh Satya Indian Institute of Technology, Delhi, India Md Shamsujjoha Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh Pallavi Sharma ABES Engineering College, Ghaziabad, India Kanithi Vakula Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, India Vishwas Yadav ABES Engineering College, Ghaziabad, India Jinhong Yang Department of Healthcare IT, Inje University, Gimhae, South Korea xv Preface Nowadays, the biggest technological challenge in big data is to provide a mechanism for storage, manipulation, and retrieval of information on large amounts of data In this context, the healthcare industry is also being challenged with difficulties in capturing data, storing data, analyzing data, and data visualization Due to the rapid growth of the large volume of information generated on a daily basis, the use of existing infrastructure has become impracticable to handle this issue So, it is essential to develop better intelligent techniques, skills, and tools to automatically deal with patient data and its inherent insights Intelligent healthcare management technologies can play an effective role in tackling this challenge and change the future for improving our lives Therefore, there are increasing interests in exploring and unlocking the value of the massively available data within the healthcare domain Healthcare organizations also need to continuously discover useful and actionable knowledge and gain insight from raw data for various purposes such as saving lives, reducing medical errors, increasing efficiency, reducing costs, and improving patient outcome Thus, data analytics in intelligent healthcare management brings a great challenge and also plays an important role in intelligent healthcare management systems In the last decade, huge advances in the large scale of data due to the smart devices has led to the development of various intelligent technologies These smart devices continuously produce very large amounts of structured and unstructured data in healthcare, which is difficult to manage in real life scenarios Big data analytics generally use statistical and machine learning techniques to analyze huge amounts of data These high dimensional data with multiobjective problems in healthcare is an open issue in big data Healthcare data is rapidly growing in volume and multidimensional data Heterogeneous healthcare data in various forms such as text, images, video, etc., are required to be effectively stored, processed, and analyzed to avoid the increasing cost of healthcare and medical errors This rapid expansion of data leads to urgent development of intelligent healthcare management systems for analysis The main objective of this edited book is to cover both the theory and applications of hardware platforms and architectures, development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research It aims to provide an intellectual forum for researchers in academia, scientists, and engineers from a wide range of applications to present their latest research findings in this area and to identify future challenges in this fledging research area To achieve the objectives, this book includes eleven chapters, contributed to by promising authors In Chapter 1, Gill et al highlighted a broad methodical literature analysis of bio-inspired algorithms for big data analytics This chapter will also help in choosing the most appropriate bio-inspired algorithm for big data analytics in a specific type of data along with promising directions for future research In Chapter 2, the author’s objective is to examine the potential impact of immense data challenges, open research issues, and distinctive instrument identification in big data analytics In Chapter 3, the author includes every possible terminology related to the idea of big data, healthcare data, and the architectural context for big data analytics, different tools, and platforms are discussed in details xvii xviii Preface Chapter addresses a machine learning model to automate the classification of benign and malignant tissue image In Chapter 5, the author describes the use of multimedia and IoT to detect TTH and to analyze the chronicity It also includes the concept of big data for the storage and processing the data, which will be generated while analyzing the TTH stress through the Internet of Things (IoT) Chapter discusses how to train a fMRI dataset with different machine learning algorithms such as Logistic Regression and Support Vector Machine towards the enhancement of the precision of classification In Chapter 7, the authors developed a prototype model for healthcare monitoring systems use the IoT and cloud computing These technologies allow for monitoring and analyzing of various health parameters in real time In Chapter 8, Onik et al includes an overview, architecture, existing issues, and future scope of blockchain technology for successfully handling privacy and management of current and future medical records In Chapter 9, Sahoo et al describes the intelligent health recommendation system (HRS) that provides an insight into the use of big data analytics for implementing an effective health recommendation engine and shows a path of how to transform the healthcare industry from the traditional scenario to a more personalized paradigm in a tele-health environment Chapter 10 discussed the interactions between drugs and proteins that was carried out by means of molecular docking process Chapter 11 integrates the kidney inspired optimization and fuzzy c-means algorithm to solve nonlinear problems of data mining Topics presented in each chapter of this book are unique to this book and are based on unpublished work of contributing authors In editing this book, we attempted to bring into the discussion all the new trends and experiments that have been performed in intelligent healthcare management systems using big data analytics We believe this book is ready to serve as a reference for a larger audience such as system architects, practitioners, developers, and researchers Nilanjan Dey Techno India College of Technology, Rajarhat, India Himansu Das KIIT, Bhubaneswar, India Bighnaraj Naik VSSUT, Burla, India Himansu Sekhar Behera VSSUT, Burla, India Acknowledgments Completing this edited book successfully was similar to a journey that we had undertaken for several months We would like to take the opportunity to express our gratitude to the following people First of all, we wish to express our heartfelt gratitude to our families, friends, colleagues, and well-wishers for their constant support and cooperation throughout this journey We also express our gratitude to all the chapter contributors, who allowed us to quote their work in this book In particular, we would like to acknowledge the hard work of authors and their cooperation during the revisions of their chapters We indebted to and grateful for the valuable comments of the reviewers that have enabled us to select these chapters out of the many chapters and also improve the quality of the chapters We are grateful for the help that was extended from the Elsevier publisher team for their continuous support throughout the entire process of publication xix 270 CHAPTER 11 KIDNEY-INSPIRED ALGORITHM AND FUZZY CLUSTERING that will possibly present a solution for a specified optimization problem The balance between intensification and diversification has a substantial result on the competence of a metaheuristic It can be an incentive for initiating the KA (kidney-inspired algorithm) The KA, which is a new population-based metaheuristic optimization algorithm inspired by the kidney process in the human body, is an extremely contemporary optimization algorithm developed by Jaddi et al [24] in 2017 The KA performs additional efficiently when compared to the current evolutionary algorithms However, KA was meant for constraint search spaces This algorithm was completely based on the filtration, reabsorption, secretion, as well as excretion processes that take place in the kidneys of a human body According to the original KA, the solutions will be sieved in an amount that is premeditated based on intent roles of all explorations in the existing population of each repetition The sifted solutions as the improved resolutions are stimulated to FB (filtered blood) and the remaining are transported to waste, which is represented by the bad solutions This is a recreation of the glomerular filtration procedure in the kidney The results that are not used will be reassessed in the iteration if they assure the rate of filtration If not, this can be excluded from the waste solution, thus replicating the excretion and reabsorbing functions of the kidney Subsequent to assignment of all the clarifications in the solutions, the greatest of them is ranked and filtration products and waste blood are combined to form a novel population and the rate of filtration can be modernized This KA was inspired by the kidney process in a human body and it is a population-based algorithm In the kidney, urine configuration consists of four steps: (1) filtration, (2) reabsorption, (3) secretion, and (4) excretion In filtration, it absorbs the transmission of both the solutes and water into the tubules from the blood in the kidneys The association of functional solutes and water against the tubules and reverse into the blood is reabsorption In the process of secretion, the tubules emit additional and harmful matter into the tubules Finally, in the excretion process, the waste matter produced during the previous three steps departs the body by means of urine The above-mentioned four steps were taken into consideration in the projected traditional KA KA begins with a solution of solutes and water elements (solutions or particles) At each iteration, the percolated solutes rely on a percolation rate that is based on objective values of all the solutes The percolated solutes are processed to FB and the rest are moved to (W) waste The above steps replicate the glomerular filtration procedure in the kidneys Absorption, excretion, and secretion are the remaining three steps in the filtration process of the kidney A solute allotted to W is again absorbed if it is to be part of FB after pertaining the reabsorption operator, if not it is evacuated from the waste Additionally, a solution in FB is secreted if it is not upgraded to the bad solution in FB W and FB are combined to be the novel population and the filtration charge is reorganized In such an algorithm, the invention of a reabsorption operator and a novel explanation is premeditated based on the existing solution and the finest solution set-up so far Here, diversification is accomplished through the process of filtration and intensification is presented by the novel solution reabsorption and generation process Solving data mining problems (especially clustering problems) has always been a tedious task for all researchers, due to the unsupervised nature There are so many efficient and robust techniques developed to handle the nonlinearity in the data irrespective of the nature and attributes of the data At the same time, there is always scope to develop the models with the latest developments in the form of algorithms/programs that are able to perform/aid the model better than the earlier versions Although a number of techniques have been developed to solve clustering problems with both K-means (Clustering Algorithm) and Fuzzy C-Means (FCM) Clustering Algorithm, issues such as initial trapping, slow convergence, higher execution time, algorithmic complexity, etc still need to be solved Apart 11.3 KIDNEY-INSPIRED ALGORITHM 271 from these, complexity and algorithmic parameters have always been important factors for any method According to “No free lunch theorem,” no algorithm will solve all the problems and will be suitable for all environments Keeping this in view, this paper focuses on a novel method of clustering with the integration of two efficient techniques: FCM and a recently developed KA It is a novel and initial attempt of hybridizing both to achieve efficient results The remaining parts of this paper are segregated into the following parts: Section 11.2 describes the biological structure and inspiration of the KA Section 11.3 elaborates on details of the KA A few important outcomes from past studies and literature using KA are highlighted in Section 11.4 Section 11.5 proposes the hybrid FCM and KA method Experimental set-up and results analysis are explained in Section 11.6 Finally, Section 11.7 concludes the work with future directions 11.2 BIOLOGICAL STRUCTURE OF THE KIDNEY The kidney is a major organ that is part of the urinary system in the human body They usually clean blood and remove surplus waste and water through the urine They also balance the quantity of ions in the blood The nephron is the main efficient element in the kidney Each kidney consists of more than a million nephrons Every nephron has a sifting system made up of a glomerulus and a tubule, through which the cleaned fluid passes Formation of the urine begins in the glomerular tubes In the glomerular capillaries, the dissolved materials are absorbed into the tubule as a result of the strength of blood pressure (BP) and the force in the Bowman’s capsule, which is a thin membrane having same composition like blood plasma The kidney’s tubule is responsible for reabsorption as well as secretion Reabsorption is the process of transporting solutes from the tubules and adding them back to the bloodstream The procedure of moving solutes into the renal tubule for excretion into the urine is known as the secretion process Elements such as hydrogen ions are able to be eliminated in the process of secretion The kidney process can be summarized in the following four steps: • • • • Filtration: transferring water and solutes from blood to the tubule Reabsorption: used to transport useful solutes and water from the tubule back into on the bloodstream Secretion: transporting surplus and harmful material into the tubular from the blood Excretion: production of the urine By following this repetitive process, the damaging and surplus substances that form the urine, are removed from the bloodstream 11.3 KIDNEY-INSPIRED ALGORITHM The projected KA is a population-based technique, so it has some individuality in common with other population-inspired algorithms Moreover, it reproduces a number of the measures in the biological structure of the kidney The four main mechanisms [24] of the kidney process are used in this algorithm 272 CHAPTER 11 KIDNEY-INSPIRED ALGORITHM AND FUZZY CLUSTERING Also, it is governed by two mathematical formulations: (i) one for movement of solutes (Eq 11.1) and (ii) another for the learning controlling parameter called filtration rate (fr) (Eq 11.2) Xi t + 1ị ẳ Xi tị + randXbest tị À Xi ðtÞÞ (11.1) Here, Xi(t) is the solute at tth iteration, Xi(t + 1) is the solute at t + 1th iteration, and Xbest(t) is the best solute at tth iteration (solute referred as candidate solution in population in KA) rand(.) is a function to generate random numbers n X fr ẳ f Xi ị iẳ1 n (11.2) Here, fr is the filtration rate, α is the learning rate, f(Xi) is the objective function on candidate solution Xi, and n is the population size In the preliminary step of the KA, an arbitrary population of solutes (candidate solutions) is produced and the evaluation of function (objective) is considered for all of them At every iteration, a novel solution is produced for all applicant solutions by movement in the direction of the finest solution (Eq 11.1) found so far Then, by pertaining the filtration operator (with high eminence), the population is cleaned into FB and the remaining are moved into W The excretion, reabsorption, and secretion systems of the biological kidney process are replicated in this process by examining some circumstances entrenched in the algorithm If an aspirant solution is allocated to W, the algorithm provides this solution an additional chance to advance itself so it can be moved into FB If this prospect is used, then the solution is excreted from the waste and an arbitrary solution is added to it If, subsequent to filtration, a result is allocated to FB and the worth of this solution is not improved compared to the worst solution in FB, this worst solution is secreted from the FB when the solution is more desirable than the worst In conclusion, the solutions in FB are positioned and the finest resolution is modernized The FB and W are combined and this modernizes its filtration rate This repetitive procedure is sustained until the extinction principle is met 11.4 LITERATURE SURVEY A limited number of applications and variations came into existence after the implementation of the KA Some of them are discussed here Taqi and Ali [25] introduced OBKA-FS: an oppositionalbased binary kidney-inspired search algorithm for feature selection In their study, a threefold development in the obtainable KA is anticipated First, BKA-FS (binary version of the kidneyinspired algorithm for feature subset selection) is initiated to progress classification accuracy in multiclass categorization problems Second, the proposed binary version of the kidney-inspired algorithm for feature subset selection (BKA-FS) is incorporated into an oppositional-based initialization technique in order to begin with good preliminary solutions Thus, this enhanced algorithm performed as OBKA-FS Last, a novel association stratagem based on the computation of MI (mutual information), which provides OBKA-FS with the ability to work in a distinct binary environment is projected For assessment, authors performed an experiment with 10 UCI machine learning standard examples Results show that OBKA-FS attained improved correctness with the same or fewer features and higher dependence with fewer redundancies Thus, the consequences 11.4 LITERATURE SURVEY 273 corroborate the elevated performance of the enhanced kidney-inspired algorithm in resolving feature selection, which is an optimization problem Ehteram et al [26] proposed the reservoir operation by a new optimization algorithm: kidney algorithm Their proposed article showed an application of the kidney algorithm for reservoir operation, that utilizes three different operators: excretion, filtration, and secretion, which produces more accurate solutions and faster convergence The author’s study contrasted reservoir operation optimization with KA, which is a new optimization algorithm The consequences illustrate that normal objective function principles and computational time for KA were all fewer than those found in the Genetic Algorithm [27], Shark Algorithm [28], Weed Algorithm [7], Bat Algorithm [29], and Particle Swarm Optimization [30] In their present study, the authors applied the Borda method and demonstrated that KA had great results, attaining the top rank as compared to the other models Their study proved that KA outperformed the remaining algorithms and addressed their defects to engender optimal operation regulation for decision-making aspects and reservoir systems A wide range of real-life problems were solved with the appropriate use of MLP-ANN (Multilayer Perceptron Artificial Neural Networks) There is good support for optimization methods in artificial neural networks for selecting the proper weights and accomplishing accurate outcomes A newly enhanced optimization method, which is a variation of the KA, can solve the problems of prediction of time series as well as classification Additional intensification is observed in the original KA due to the scenario that more solutes are sieved and returned to sieved blood In contrast, if extra solutes lead to despoil which showcase the effective diversification A newly developed optimization method for simulating neural networks was introduced by Jaddi et al [31] in 2018 They described the problem of rainfall time series prediction with the suitable balance of intensification and diversification To evaluate the performance of their developed method, the authors considered several standard datasets Their experimental results showcase the effective performance and proved their method is robust and can be used to solve real-life forecasting problems Ekinci et al [32] developed a method for solving the tuning problem of power system stabilizers (PSS) using a recently developed population-based algorithm named KA They used KA mainly to search for the best parameters They sustained the problem of the power system by introducing an Eigen value coefficient The authors considered 16 machines as well as 68 bus power systems to evaluate the performance of their developed method Later, they compared their intended method with some highly recommended and standard algorithms such as the ancient PSO (Particle Swarm Optimization) algorithm as well as the BA (Bat Algorithm) They claimed that their experimental results outperformed the compared methods Homaid et al [33] introduced a kidney algorithm for pairwise test suite generation Pairwise testing can significantly reduce the rate of software testing and also raise the capability of fault detection Metaheuristic algorithms have been mostly used for resolving complicated problems of optimization as well as showing their efficacy to obtain nearly all optimal solutions Their study initiates a new pairwise strategy by adapting the KA This is the first example of adjusting the KA to produce a test suite The author’s projected approach is known as the PKS (Pairwise Kidney Strategy) Their study also highlights the pairwise kidney strategy design In the same way, they contrasted their proposed system with other detailed approaches in the literature in the provision of test suite sizes Finally, in their proposed method, the experiment results illustrated that PKS (pairwise strategy) had very competitive outcomes when compared to the remaining strategies 274 CHAPTER 11 KIDNEY-INSPIRED ALGORITHM AND FUZZY CLUSTERING 11.5 PROPOSED MODEL This proposed model for biomedical data analysis makes use of KA (Section 11.3) and FCM clustering (Section 11.5.1) 11.5.1 FUZZY C-MEANS ALGORITHM This algorithm allows belongingness of one piece of data to several clusters It is a partitional algorithm based on the minimization of one objective function (Eq 11.3) [34] with regards to the partition matrix J U, V ị ẳ C X N X  2 uij m xj À vi  (11.3) i¼1 j¼1 where “xj” and “vi” indicates the jth cluster point and ith cluster center respectively ui, j indicates the membership value of jth cluster point with regards to cluster “i.” “m” is the fuzzy controlling factor It may result in hard partition when setting the value as “1” and complete fuzziness when setting the value as “∞.” kk is the norm function The FCM algorithm works with three main factors [35]: fuzzy membership function, partitional matrix, and objective function uij m is calculated as described in Eq (11.4) and the cluster center as in Eq (11.5)  ! # c xj À vi  À1 X   mÀ1 Uij ¼ xj À vk  k¼1 " N X (11.4) , uij m xj vi ¼ j¼1 where i ! 1, i N X c (11.5) uij m j¼1 This method is almost equivalent to the K-means algorithm except for the factor uij m (fuzziness factor) The fuzziness factor determines the level of fuzziness for the clusters The results of FCM depends on the initial choice of values for the clusters, which is one of the disadvantages of this system The detailed algorithm steps of FCM are described in [36] 11.5.2 PROPOSED KA-BASED APPROACH FOR BIOMEDICAL DATA ANALYSIS In this section, a metaheuristics approach based on the KA and FCM (KA-FCM) has been developed for cluster analysis in biomedical data Existing biomedical data are usually nonlinear and complex in nature, which contains many clusters in data while applying FCM clustering [37] Although FCM has been found to be successful in biomedical data clustering, there is always a chance of further improvements due to the randomness in initial cluster center selection (Eq 11.4) This proposed work has been carried with the objective to help the FCM in cluster analysis on biomedical data by making available optimal cluster centers, which enable the FCM to have initial optimal clusters rather than random clusters This proposed approach has been observed as efficient not only in faster convergence but also in forming qualitative clusters The synthesized working schema of this projected method is demonstrated in Fig 11.1, which has two major phases, i.e., phase is the process of obtaining optimal cluster centers using KA (Section 11.5.2.1) and phase is the method for cluster analysis using optimal cluster centers (Section 11.5.2.2) 11.5 PROPOSED MODEL 275 Start Initialize the population of cluster centers (solutes) “P” Ctr = Movement of solute by using Eq (11.1) Compute fr by using Eq (11.2) Ctr = Ctr+1 Evaluate fitness ‘f ’ of each solute using Eq (11.6) Yes Filtered blood (FB) IS solute better than worst solute in “P” Yes No IS f > fr Waste (W) Update “P” by using FP No No Is stopping criteria is met Movement of solute by using Eq (11.1) Evalute fitness ‘f ’ of each solute using Eq (11.6) No Yes Yes Yes Is solute is better than worst solute in “P” Find best solute from “P” IS f > fr No Stop Add randomness on solutes and generate new solute Yes Is solute better than worst solute in “P” No FIG 11.1 Getting optimal cluster centers 11.5.2.1 Obtaining optimal cluster centers È using KA É Initially, the population of cluster centers Pitr ¼ C1 itr , C2 itr , …Cn itr was generated randomly, where Ci itr denoted ith vector of cluster centers (referred to as solute in the kidney) at iteration “itr” i.e., the jth cluster center of the ith vector of cluster Ci itr ¼ fci,1 itr , ci,2 itr , …ci,m itr g Here ci, j itr symbolized È É centers Ci itr , which may be visualized as ci, j itr ¼ ci, j,1 , ci, j,2 , …ci, j, d , d ¼ dimension of the dataset For all Ci itr , their fitness is calculated using Eq (11.6) as follows: FðCi Þ ¼ k m X n À X j¼1 r¼1 or À ci, j Á2 ! (11.6) +d 276 CHAPTER 11 KIDNEY-INSPIRED ALGORITHM AND FUZZY CLUSTERING Here, “Ci” indicates the ith candidate solution in “P.” F(Ci) indicates the fitness of “Ci.” “n” and “m” represent the number of instances of a particular dataset and number of cluster centers “or” represents the rth instance of the dataset, “ci,j” is the jth cluster center of ith (Ci) solution in the population Here “k” is a constant and “d” is a small valued constant The term “or Àci,j” calculates the Euclidean distance, where “or” is the data object and “ci,j” is the cluster center The value of “d” is set to 0.1 Using F(Ci), the intra cluster distance is minimized Based on the fitness of each Ci itr , the best and worst cluster center are selected as Cbest itr and Cworst itr respectively In the solute movement phase, Cbest itr has been used for improvising the solution in the population P by using Eq (11.1) as follows: C0i ¼ Ci + randðCbest À Ci Þ (11.7) In Eq (11.7), Ci, Ci, and Cbest are the ith cluster center vector after the movement, the previous ith cluster center vector, and the best cluster center vector (discovered so far) in the population P, respectively After the solute movement process, the resultant solutes (cluster centers) may be visualized as 0 Mitr ¼ {C1, C2, …Cn}, Mitr holds all cluster center vectors on iteration “itr.” Filtration rate (fr) is computed on P using Eq (11.2) as follows: F ð C Þ i C B C Bi¼1, Ci 2P C fr ¼ α  B C B n A @ n X (11.8) In Eq (11.8), objective function F(Ci) is calculated for each Ci using Eq (11.6) If new cluster center vectors Ci satisfied fr, i.e., if (rand(1) ! fr) then it is considered for FB If not, it is added to W Before adding into FB, it is tested with the worst solution Cworst, i.e., if F(Ci) is greater than or equal to F0 (Cworst), then Ci is added into FB If not, it is added into W After that, all the Ci that are added into W go through the excretion process and all the Ci that are added into FB go through the secretion pro0 cess All the Ci go through the movement of solute process using Eq (11.1) as in Eq (11.7), and are checked with fr If they satisfy the fr and are better than Cworst, then they are added into FB If not, they 0 are added into W after random modification on Ci (i.e., Ci ẳ Ci ặ rand (1)) After completion of the selection process of FB, the population P is updated using FB This complete process is iterated until the stopping criteria are met or maximum iteration is reached Finally, the best solution Cbest itr from the population P is selected, where Cbest itr is a vector of cluster centers Consequently, this obtained cluster centers is used with FCM for cluster analysis 11.5.2.2 Cluster analysis using optimal cluster centers Unlike the original working mechanism of FCM, which starts with randomly initialized cluster centers, the obtained cluster centers from Section 11.5.2.1 are assigned as initial cluster centers of FCM while performing clustering With these cluster centers, the fuzzy partition matrix was evaluated using Eq (11.4) and the fuzzy membership objective value was observed Then, the fuzzy cluster centers are updated (Eq 11.5) according to the fuzzy partition matrix If a change in fuzzy objective values in successive iterations is found to be insignificant, then the FCM iteration is stopped and the resultant partition matrix is used for cluster analysis on the considered dataset 11.6 RESULTS ANALYSIS 277 11.6 RESULTS ANALYSIS The projected approach was executed in MATLAB and all the perspectives were assessed on eight numbers of biomedical datasets from the UCI repository [38] and Kentridge repository [39] The datasets are considered as same as in [40] 11.6.1 EVALUATION METRICS In this study, to evaluate the potential of the anticipated technique, two evaluation metrics (objective function and accurateness) were measured Accuracy (Eq 11.9) is the proportion of the number of true positives and negatives for the whole number of instances Accuracy ¼ TP + TN TP + TN + FP + FN ! (11.9) Here, the terms TP, TN, FP, FN stand for true positive, true negative, false positive, and false negative In addition to that, the standard of clusters produced through the algorithm can be specified by clustering accuracy (Eq 11.10) Clustering Accuracy ¼ No: of correctly sampled data  100% Total no: of sample (11.10) The clustering accuracy was calculated for every dataset and the proposed method was compared with the outcomes 11.6.2 EXPERIMENTAL RESULTS Experimental analysis was performed using six techniques comprising the anticipated method Outcomes were attained for all the datasets As the proposed method is based on clustering and the aspect of performance is accuracy, the essential standard technique of clustering known as FCM and several additional techniques (SVM, Naive Bayes, Back propagation neural network (BPNN), and decision tree) were measured for the experimental assessment Every technique was trialed for 50 independent runs and the consequences were reproduced As the composite approach was utilized to discover the preeminent clusters, i.e., it is obliging to evade from fixed at local minima that is predominantly occurs in FCM However due to the initialization is throughout preeminent cluster hubs, the projected advance was well-organized when compared to FCM The alterations in objective functions in every iteration were directly proportional to the number of iterative loops in any algorithm Here the intention is to address the issues related to the number of iterations and objective function It was obvious that the efficiency of the projected technique was somewhat superior to FCM In Table 11.1, the average accuracy of all the methods is provided and the end result is that the anticipated FCM-KA method is better than many other techniques In the Thyroid and Dermatology datasets, it is obvious that the recommended method has the highest accuracy of 96.81% and 98.42% respectively The superiority of the proposed method is clearly represented in in terms of accuracy The projected method was compared with several works on similar datasets (Fig 11.2) As shown in four other modern studies, the projected FCM-KA system showed better precision Moreover, the outcome 278 CHAPTER 11 KIDNEY-INSPIRED ALGORITHM AND FUZZY CLUSTERING Table 11.1 Comparison of All the Methods Based on Their Average Accuracy Average Accuracy (in %) Datasets FCM-KA FCM Naive Bayes SVM Decision Tree BPNN Dermatology Heart Ecoli Haberman Liver Hepatitis Pima Thyroid 98.42 95.19 84.66 94.75 94.83 96.2 94.47 96.81 90.4 84.2 84.3 81.6 76.1 88 90.2 88.2 95.8 75.2 80.7 70.6 53 89.5 82.4 89.2 87.1 76.4 78.2 65.4 62 83.2 89.5 86.4 82 70.4 77.5 64.2 55.14 82.25 76.2 91.2 84.3 72.2 82 60.6 60.8 80.5 87.3 86.8 FIG 11.2 Performance comparison of FCM-KA with other standard techniques of the proposed method in other datasets such as liver and Haberman was more efficient than [39], but for all the residual datasets, the results of FCM-KA were positive 11.6.3 STATISTICAL VALIDITY To demonstrate the statistical importance of the projected method, the Friedman rank test [41] and Iman-Davenport test [42] were applied Ranks (Table 11.2) were allocated to all the classifiers and the standard ranks were calculated Additional information about this test is illustrated in [43] With diverse statistical features such as z-value, p-value, and critical factor, the assumption was discarded in all the cases (Table 11.3) This shows that the projected method is statistically noteworthy and achieved better results compared to the other techniques ACKNOWLEDGMENT 279 Table 11.2 Assigned Friedman Ranks for All the Classifiers Average Accuracy (in %) Datasets FCM-KA FCM Naive Bayes SVM Decision Tree BPNN Dermatology Heart Ecoli Haberman Liver Hepatitis Pima Thyroid Avg 98.42(1) 95.19(1) 84.66(1) 94.75(1) 94.83(1) 96.2(1) 94.47(1) 96.81(1) 90.4(3) 84.2(2) 84.3(2) 81.6(2) 76.1(2) 88(3) 90.2(2) 88.2(4) 2.5 95.8(2) 75.2(4) 80.7(4) 70.6(3) 53(6) 89.5(2) 82.4(5) 89.2(3) 3.62 87.1(4) 76.4(3) 78.2(5) 65.4(4) 62(3) 83.2(4) 89.5(3) 86.4(6) 82(6) 70.4(6) 77.5(6) 64.2(5) 55.14(5) 82.25(5) 76.2(6) 91.2(2) 5.12 84.3(5) 72.2(5) 82(3) 60.6(6) 60.8(4) 80.5(6) 87.3(4) 86.8(5) 4.75 Table 11.3 Results of Statistical Tests Test Name Statistical Value p-Value Hypothesis (Accepted/Rejected) Friedman Iman-Davenport 21.51519 8.147 01287 00294 Rejected Rejected 11.7 CONCLUSION Developing one high-quality optimization algorithm and using it to resolve problematic data mining tasks has been an ultimate challenge for researchers In the current work, a combined approach of FCM and the newly developed kidney-inspired algorithm was developed for biomedical applications All the measured datasets were of the biomedical type from the UCI repository Performance evaluators, such as objective function values and accurateness, were used for comparing the results of the projected approach with a number of the presented methods Also, the Friedman test was used to examine and confirm the presentation of the anticipated effort After a wide-ranging experimental analysis, it is concluded that the projected technique is able to generate hopeful results compared to other classifiers In addition, some enhanced deviations of KA can lead to further good results Also, extraction of preeminent characteristics with intelligent agents may give enhanced results As a future scope, this research can be extended by using the variations in the parameter of KA and elitist based KA can be proposed for solving various data mining problem ACKNOWLEDGMENT This research work was partially supported by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), New Delhi, Govt of India, under the research project grant (Sanction Order No EEQ/2017/000355) 280 CHAPTER 11 KIDNEY-INSPIRED ALGORITHM AND FUZZY CLUSTERING REFERENCES [1] R Hedayatzadeh, F.A Salmassi, M Keshtgari, R Akbari, K Ziarati, Termite colony optimization: a novel approach for optimizing 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Hettich, C Blake, C Merz, UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/ mlearn/MLRepository.html, 1998 [39] L Jinyan, L Huiqing, Kentridge Bio-Medical Data Set Repository, http://datam.i2r.a-star.edu.sg/datasets/ krbd/, 2002 [40] U Kanimozhi, D Manjula, An intelligent incremental filtering feature selection and clustering algorithm for effective classification, Intell Automat Soft Comput (2017) 1–9 [41] M Friedman, The use of ranks to avoid the assumption of normality implicit in the analysis of variance, J Am Stat Assoc 32 (1937) 675–701 [42] R.L Iman, J.M Davenport, Approximations of the critical region of the Friedman statistic, Commun Stat Theory Methods (1980) 571–595 [43] J Nayak, B Naik, H.S Behera, A novel chemical reaction optimization based higher order neural network (CRO-HONN) for nonlinear classification, Ain Shams Eng J (3) (2015) 1069–1091 Index Note: Page numbers followed by f indicate figures and t indicate tables A Active contour models (ACM), 30 Activity-Awareness for Human-Engaged Wellness Applications (ATHENA), 50 Adafruit cloud, 180–181, 187–189, 187–188f Advanced multimodal image-guided operating (AMIGO), 47–48 Allotted records mining algorithms, 21 Alpha frequency, 87 Alzheimer’s disease (AD) gene interaction of genes, encoding proteins, and targeted drugs for, 255, 255t string database, 253, 254f molecular docking analysis, 257–262 Analysis of variance, 114t, 116t, 122t, 124t, 130t, 133t, 135, 136f, 141t, 143t Anesthesia big data, 49 Anesthesia Quality Institute, 49 Anonymization techniques, 208–210 Ant colony optimization (ACO) algorithm, Apache Hadoop, 28–29, 240 Apache Mahout, 21, 49 Apache Oozie, 54 Apache Spark, 28 Artificial bee colony (ABC) algorithm, Artificial immune system (AIS) algorithm, Artificial neural network (ANN), 232 Association for Applied Psychophysiology and Biofeedback (AAPB), 87–88, 96–97 Association rule mining, 232 Association technique, 9–10 Audio/speech analytics, 8–9, 8f AutoDock software, 256 Autonomic responses, 87 Avro, 54 B BaaS See Blockchain as a service (BaaS) Backend-as-a-service (BaaS), 14 Back propagation algorithm, 50, 155 Back propagation neural network (BPNN), 277–278, 278–279t, 278f Bacterial foraging optimization (BFO) algorithm, Bat Algorithm (BA), 272–273 Bayesian classifier, 232–233 BDA See Big data analytics (BDA) BFO algorithm for network-traffic (BFON), Big data, 151–152 characteristics, 93, 93f communication science, 249 definition, 93, 200, 247–248 Hadoop, 93 healthcare (see Healthcare big data) processing, stages in, 3–4, 3f structured/unstructured data, 247–248 value, 44, 44f variability, 44, 44f variety, 44, 44f, 200 velocity, 44, 44f, 200 veracity, 44, 44f volume, 44, 44f, 200 Big data analytics (BDA), 152 allotted records mining algorithms, 21 arrangement of challenges anomalies in human ecosystems, detection of, 26 probabilistic method, 25 user intervention method, 25 bio-inspired algorithms, 10 audio/speech analytics, 8–9 challenges, 10–13 comparisons of, 10, 11–12t ecological algorithms, 7–10 evolutionary algorithms, 4–5 evolution of, 7, 7f parameters of, 9–10, 9f predictive analytics, research areas, 14–15 social media analytics, swarm-based algorithms, 6–7 taxonomy of, 5f text analytics, time count of, 7, 8f video analytics, consumable massive facts analytics, 19–20 data management, dimensions of, 2, 2f data sources, 23, 23f facts aggregation challenges, 21 gadget failure, 21 healthcare (see Healthcare big data) huge record management, demanding situations in, 26–27 image mining and processing, 29–31 information integration challenges, 22–23 massive facts, 27–28 283 284 Index Big data analytics (BDA) (Continued) model, 2–4, 3f records analysis challenges Bayesian people group, 24 pattern interpretation challenges, 24 scale of statistics, 24 records-variety trouble, capacity solutions for, 29 scalability assignments, solutions for, 33–36 spark structures, 29 statistics preservation-demanding situations, 22 variety of data, 2, 2f velocity trouble, solutions for massive actualities calculations, 32 privateers and safety undertaking, 32–33 statistics representation, 32 transactional databases, 31–32 Big Data Based Recommendation Engine, 49 Binary version of the kidney-inspired algorithm for feature subset selection (BKA-FS), 272–273 Biofeedback (BF) AAPB, 87 aim of, 88 chronic pain and stress symptoms, reduction of, 88 definition, 88, 96–97 instruments, 88 Kamiya, Joe, 87 mind-body and consciousness measurements, 96f mood states, factors of, 97f rays emitted by individual, 96t stages of daily routine, 95 pneumatic biofeedback devices, 87–88 sensor modalities, 96–97 treatment of anxiety, 87–88 chronic type TTH stress (see Tension type headache (TTH)) migraines, 95 psychosomatic disorders, 88 voluntary control, 88 Biofeedback Certification Institute of America (BCIA), 88, 96–97 Biofeedback Research Society (BRS) See Association for Applied Psychophysiology and Biofeedback Biofeedback Society of America (BSA) See Association for Applied Psychophysiology and Biofeedback Biogeography-based optimization (BBO) algorithm, Bio inspired algorithms (BIAs), big data analytics, 10, 269–270 challenges, 10–13 comparisons of, 10, 11–12t ecological algorithms, 7–10 evolutionary algorithms, 4–5 evolution of, 7, 7f parameters of, 9–10, 9f research areas, 14–15 swarm-based algorithms, 6–7 taxonomy of, 5f text analytics, time count of, 7, 8f types of, 8f audio/speech analytics, 8–9 predictive analytics, social media analytics, text analytics, video analytics, Biological data, 249 Biomedical data analysis, FCM-KA method cluster analysis using optimal cluster centers, 274, 276 objective function values and accurateness, 277 obtaining optimal cluster centers, 274–276, 275f vs standard techniques, average accuracy, 277–278, 278t, 278f statistical validity, 278, 279t Biometric data, 208 Bitcoin, 203–205 Bitcoin as a service (BiaaS), 15 Blockchain as a service (BaaS), 14 Blockchain health, 219–220 Blockchain technology architecture of, 202, 203f benefits of, 199, 199f, 213, 214f big data challenges vs opportunities, 213, 215f capability of, 213, 216t challenges and solutions, 217–221 characteristics of, 202 collaborative patient engagement, 216–217 consensus algorithm, 205t, 206 consent, 199 cybersecurity, 213–214 data integrity, 198–199 decentralized storage, 199 digital signature, 206 digital supply chain, 213 digital trust, 210–211 fighting counterfeit drugs, 216 forking, 204f, 206 GDPR, 218–221 hash algorithm, 206 headers, 205 health claims, 214 immutability, 199 increased capacity, 199 intelligent data management, 212 interoperability and data sharing, 214–215 market, 202–205, 203f medical and IoT devices, 213 medication adherence, 214 Merkle tree root hash, 204f, 205 Index off-chain data storage, 217 online access to longitudinal data, 217 prerequisites, 206 research and development, 215 smart ecosystem, 212 transaction counter, 205 transaction data, 205 types of, 204f, 206 versions of, 202 working principle of, 206, 207f Bluetooth Low Energy, 179 Borda method, 272–273 Breast cancer tissue image classification, ConvNets accuracy ranges, 60 architecture, 61, 62f dataset and methodologies BreaKHis image dataset, 59–60, 61f convolution layer, 61 dimensionality reduction, PCA algorithm, 59–60, 62–63 fixed feature extractor, 62, 63f fully connected layer, 61 K-NN algorithm, 59–60, 64, 65f logistic regression, 59–60, 63 pooling layer, 61 SVM, 59–60, 64, 64f transfer learning, 62 10-fold cross validation accuracy, 67, 68t implementation classification, 67 dimensionality reduction, PCA algorithm, 66, 67t feature extraction, 66, 66t hyperparameter tuning, 67 Python programming language, 66 proposed model, 64–65, 65f test performance, LR, SVM, and K-NN 40 data, 71–73 100 data, 73–76 200 data, 77–80 400 data, 80–82 validation accuracy, LR, SVM, and K-NN best validation accuracy, 69, 70t 40 data, 67, 68f 100 data, 68, 69f 200 data, 68, 69f 400 data, 69, 70f performance on test set, 69–70 Burst IQ, 220 C CaaS See Container as a service (CaaS) CARE, 49 Cassandra, 9–10, 53, 240 Cat swarm optimization (CSO) algorithm, 285 Chan-Vese (CV) technique, 30 Chemical carcinogenicity, 154 Chronic migraine (CM), 95 Chronic obstructive pulmonary disease (COPD), 48 Chronic tension type headache (CTTH), 95 Clinical data, 45 Cloud computing, 1, 248 healthcare data optimization, 197–198 and IoT-based health monitoring system act/notify module, 181–183, 182f Adafruit cloud registration process, 180–181, 187–189, 187–188f application layer, 179 body temperature data storage, 189–191, 194f body temperature reading, 183–184, 184f, 189, 189–192f detecting layer, 179 fetch module, 179–180, 180f health data, 179 heart rate, 183, 184f ingest module, 180, 181f live body temperature reading in screen, 189, 193f medicinal services framework, 178 pulse rate graph, 184, 186f raw pulse rate value, 184, 185f retrieve module, 181, 181f temperature reading on serial monitor, 183–184, 184f total feed of temperature reading, 189, 193f transport layer, 179 NAS, 33 PaaS, SaaS, IaaS, HaaS, 33–34 Cloud data centers (CDCs), 13 Cloudera Oryx, 21 Cluster analysis, 152 Clustering, 9–10, 232 Collaborative filtering (CF) recommendation system, 230–231f e-commerce websites, use in, 230 HRS, 242 memory-based collaborative filtering, 231 model-based collaborative filtering, 232 recommendation and prediction, 231 Compound annual growth rate (CAGR), 200–201, 203–205 Consensus algorithm, blockchain, 205t, 206 Consumable massive facts analytics, 19–20 Container as a service (CaaS), 14 Content-based filtering technique, 229 Convolution neural networks (CNNS/ConvNets) See Breast cancer tissue image classification, ConvNets Cosine-based similarity measures, 232 Couchbase, 9–10 CouchDB, 248 Counterfeit drugs, 216 Cryptocurrencies, 15 Cuckoo search optimization (CO) algorithm, Cybersecurity, 213–214 ...Big Data Analytics for Intelligent Healthcare Management Advances in Ubiquitous Sensing Applications for Healthcare Big Data Analytics for Intelligent Healthcare Management Volume... Training data Model generation Data sampling Data clustering Training data Clean data Feature extraction Dimension aggregation Data cubes FIG 1.3 Big data analytical model Big data process Data management. .. required data from unstructured data There are five types of analytics for big data management using bio-inspired algorithms: text analytics, audio analytics, video analytics, social media analytics,