This book highlights various evolutionary algorithm techniques for various medical conditions and introduces medical applications of evolutionary computation for real-time diagnosis.
Evolutionary Intelligence for Healthcare Applications presents how evolutionary intelligence can be used in smart healthcare systems involving big data analytics, mobile health, personalized medicine, and clinical trial data management. It focuses on emerging concepts and approaches and highlights various evolutionary algorithm techniques used for early disease diagnosis, prediction, and prognosis for medical conditions. The book also presents ethical issues and challenges that can occur within the healthcare system.
Researchers, healthcare professionals, data scientists, systems engineers, students, programmers, clinicians, and policymakers will find this book of interest.
| Preface |
|
ix | |
| Acknowledgments |
|
xiii | |
| About the Authors |
|
xv | |
|
1 Evolutionary Intelligence |
|
|
1 | (10) |
|
|
|
1 | (1) |
|
|
|
2 | (1) |
|
1.2.1 Evolutionary Computation |
|
|
2 | (1) |
|
1.3 Evolutionary Algorithms |
|
|
2 | (6) |
|
1.3.1 Representation of EA |
|
|
3 | (1) |
|
1.3.2 Components of Evolutionary Algorithms (EAs) |
|
|
4 | (1) |
|
1.3.3 What Varieties of EA Are There to Choose from? |
|
|
5 | (1) |
|
1.3.4 Typical EA Pseudo Code |
|
|
6 | (2) |
|
1.4 Role of EA in Healthcare |
|
|
8 | (1) |
|
|
|
9 | (2) |
|
|
|
9 | (2) |
|
2 Heart Disease Diagnosis |
|
|
11 | (20) |
|
|
|
11 | (1) |
|
|
|
12 | (2) |
|
|
|
13 | (1) |
|
|
|
13 | (1) |
|
2.2.3 Heart Value Complications |
|
|
13 | (1) |
|
2.2.4 Hypertension -- Heart Disease |
|
|
14 | (1) |
|
2.3 Heart Disease Classification Using EA |
|
|
14 | (5) |
|
|
|
15 | (1) |
|
|
|
16 | (1) |
|
|
|
16 | (1) |
|
|
|
17 | (1) |
|
2.3.5 Forward Feature Selection |
|
|
17 | (1) |
|
2.3.6 Backward Feature Elimination |
|
|
18 | (1) |
|
|
|
18 | (1) |
|
2.3.8 LASSO Regularization (L1) |
|
|
18 | (1) |
|
2.3.9 Random Forest Importance |
|
|
18 | (1) |
|
2.4 Challenges and Issues in Heart Disease Diagnosis |
|
|
19 | (5) |
|
2.4.1 Traditional Systems |
|
|
19 | (1) |
|
2.4.2 Existing Methodologies for Diagnosing Heart Diseases |
|
|
20 | (4) |
|
2.5 EA for Heart Disease Diagnosis |
|
|
24 | (4) |
|
|
|
28 | (3) |
|
|
|
28 | (3) |
|
3 Diabetes Prediction and Classification |
|
|
31 | (18) |
|
|
|
31 | (1) |
|
|
|
32 | (1) |
|
3.3 Type 2 Diabetes Mellitus |
|
|
32 | (1) |
|
|
|
33 | (1) |
|
3.5 The Different Diabetes Types |
|
|
34 | (5) |
|
3.5.1 Retinopathy and Associated Disorders |
|
|
34 | (2) |
|
3.5.2 Renal Pathology Nephropathy |
|
|
36 | (2) |
|
|
|
38 | (1) |
|
|
|
39 | (3) |
|
|
|
40 | (1) |
|
|
|
41 | (1) |
|
3.6.3 Soft-Consistency Effluents |
|
|
41 | (1) |
|
|
|
42 | (2) |
|
3.8 Blood Vessel Division and Segmentation |
|
|
44 | (1) |
|
|
|
45 | (4) |
|
|
|
45 | (4) |
|
|
|
49 | (16) |
|
|
|
49 | (5) |
|
4.1.1 Neurodegenerative Disease Classification |
|
|
50 | (4) |
|
4.2 Early Prediction of Neurodegenerative Disease and Challenges |
|
|
54 | (2) |
|
4.2.1 Early Prediction -- Alzheimer's Disease |
|
|
54 | (1) |
|
4.2.2 Early Prediction -- Parkinson's Disease |
|
|
55 | (1) |
|
4.3 EA for Treating Degenerative Disorders |
|
|
56 | (5) |
|
4.3.1 Genetic Algorithms in Diagnosing Degenerative Disorders (DD) |
|
|
57 | (4) |
|
|
|
61 | (4) |
|
|
|
62 | (3) |
|
|
|
65 | (14) |
|
|
|
65 | (2) |
|
5.2 Tuberculosis Classification |
|
|
67 | (5) |
|
|
|
68 | (1) |
|
|
|
69 | (1) |
|
5.2.3 Challenges in Diagnosing PTB and EPTB |
|
|
69 | (3) |
|
5.3 EA for Diagnosing Tuberculosis |
|
|
72 | (3) |
|
5.3.1 Role of EA in Tuberculosis Treatment |
|
|
73 | (2) |
|
|
|
75 | (4) |
|
|
|
76 | (3) |
|
|
|
79 | (16) |
|
|
|
79 | (3) |
|
6.1.1 Causes of Muscular Dystrophy |
|
|
79 | (1) |
|
6.1.2 Types of Muscular Dystrophy |
|
|
80 | (1) |
|
6.1.3 Diagnosing Muscular Dystrophy |
|
|
80 | (1) |
|
6.1.4 Treating Muscular Dystrophy |
|
|
81 | (1) |
|
6.1.5 Common Muscular Dsytrophy |
|
|
82 | (1) |
|
6.2 Early Clinical Diagnosis of MD |
|
|
82 | (4) |
|
6.3 EA for Diagnosing Muscular Dystrophy |
|
|
86 | (4) |
|
|
|
90 | (5) |
|
|
|
91 | (4) |
|
7 Tumor Prediction and Classification |
|
|
95 | (22) |
|
|
|
95 | (1) |
|
|
|
96 | (2) |
|
|
|
96 | (1) |
|
|
|
97 | (1) |
|
|
|
97 | (1) |
|
7.3 Carcinoma Classification |
|
|
98 | (8) |
|
|
|
99 | (1) |
|
|
|
100 | (2) |
|
|
|
102 | (1) |
|
|
|
103 | (1) |
|
|
|
104 | (1) |
|
|
|
105 | (1) |
|
7.4 EA for Tumor Classification |
|
|
106 | (5) |
|
|
|
107 | (3) |
|
7.4.2 Parameter Optimization |
|
|
110 | (1) |
|
7.5 EA for Carcinoma Prediction |
|
|
111 | (2) |
|
|
|
112 | (1) |
|
7.5.2 Parameter Optimization |
|
|
112 | (1) |
|
|
|
113 | (4) |
|
|
|
114 | (3) |
| Index |
|
117 | |
Dr. T. Ananth Kumar is working as Assistant Professor in IFET college of Engineering affiliated to Anna University, Chennai. He received his Ph.D. degree in VLSI Design from Manonmaniam Sundaranar University, Tirunelveli. He received his Masters degree in VLSI Design from Anna University, Chennai and Bachelors degree in Electronics and communication engineering from Anna University, Chennai. He has presented papers in various National and International Conferences and Journals. His fields of interest are Networks on Chips, Computer Architecture and ASIC design. He is the recipient of the Best Paper Award at INCODS 2017. He is the life member of ISTE, and few membership bodies. He has 8 patents in various domains. He has written many book chapters in Springer, IET Press, and Taylor & Francis press.
Dr. R. Rajmohan is currently working as Associate Professor in Vellore Institute of Technology, Andrapradesh. He completed his Ph.D. in the field of wireless network at SSN college of Engineering under Anna University. He received his Masters degree in Network and Internet Engineering from Pondicherry University, Pondicherry and Bachelors degree in Computer Science and Engineering from Pondicherry University, Pondicherry. He has published more than 20 papers in various reputed SCI and Scopus indexed journals. His fields of interest are Wireless Network, Deep learning and IoT. He has won the best educator award from International Institute of Organized Research (I2OR) in the year 2019. He is the life time member of various educational bodies and acted as reviewer for Springer and other standard journals.
Ms. M. Pavithra received her Masters degree in Distributed Computing System from Pondicherry University, Puducherry. She received her Bachelors degree in Computer Science and Engineering from Anna University, Chennai. She is working as Assistant Professor in IFET College of Engineering afflicted to Anna University, Chennai. Her area of interest is Artificial Intelligence, Network Security, Computational intelligence.
Dr. S. Balamurugan PhD, SMIEEE, ACM Distinguished Speaker is the Director of Albert Einstein Engineering and Research Labs. India. He received his B.Tech., Degree from PSG College of Technology, Coimbatore, India, M.Tech., and PhD Degrees from Anna University, India. He has published 60 books, 300 international journals/conferences and 200 patents. He is also the Vice-Chairman of Renewable Energy Society of India (RESI). He is also serving as a research consultant to many Companies, Startups, SMEs and MSMEs. He is the series editor of book series and serving in various editorial capacities of several International Journals.. He is also the recipient of the Young Scientist Award, Certificate of Exceptionalism, and Outstanding Scientist Award and Best Director Award. His biography is listed in "Marquis WHO'S WHO",USA. His research interests include Artificial Intelligence, Augmented Reality, Internet of Things, Big Data Analytics, Cloud Computing, and Wearable Computing. He is a life member of ACM, IEEE, ISTE and CSI.
More info about Taylor & Francis e-books