Soft Computing Techniques for Type-2 Diabetes Data Classification: 1st Edition (Hardback) book cover

Soft Computing Techniques for Type-2 Diabetes Data Classification

1st Edition

By Ramalingaswamy Cheruku, Damodar Reddy Edla, Venkatanareshbabu Kuppili

Chapman and Hall/CRC

168 pages | 36 B/W Illus.

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Description

Diabetes Mellitus (DM, commonly referred to as diabetes, is a metabolic disorder in which there are high blood sugar levels over a prolonged period. Lack of sufficient insulin causes presence of excess sugar levels in the blood. As a result the glucose levels in diabetic patients are more than normal ones. It has symptoms like frequent urination, increased hunger, increase thirst and high blood sugar. There are mainly three types of diabetes namely type-1, type-2 and gestational diabetes. Type-1 DM occurs due to immune system mistakenly attacks and destroys the beta-cells and Type-2 DM occurs due to insulin resistance. Gestational DM occurs in women during pregnancy due to insulin blocking by pregnancy harmones. Among these three types of DM, type-2 DM is more prevalence, and impacting so many millions of people across the world. Classification and predictive systems are actually reliable in the health care sector to explore hidden patterns in the patients data. These systems aid, medical professionals to enhance their diagnosis, prognosis along with remedy organizing techniques. The less percentage of improvement in classifier predictive accuracy is very important for medical diagnosis purposes where mistakes can cause a lot of damage to patient’s life. Hence, we need a more accurate classification system for prediction of type-2 DM. Although, most of the above classification algorithms are efficient, they failed to provide good accuracy with low computational cost. In this book, we proposed various classification algorithms using soft computing techniques like Neural Networks (NNs), Fuzzy Systems (FS) and Swarm Intelligence (SI). The experimental results demonstrate that these algorithms are able to produce high classification accuracy at less computational cost. The contributions presented in this book shall attempt to address the following objectives using soft computing approaches for identification of diabetes mellitus.

  • Introuducing an optimized RBFN model called Opt-RBFN.
  • Designing a cost effective rule miner called SM-RuleMiner for type-2 diabetes diagnosis.
  • Generating more interpretable fuzzy rules for accurate diagnosis of type2 diabetes using RST-BatMiner.
  • Developing accurate cascade ensemble frameworks called Diabetes-Network for type-2 diabetes diagnosis.
  • Proposing a Multi-level ensemble framework called Dia-Net for improving the classification accuracy of type-2 diabetes diagnosis.
  • Designing an Intelligent Diabetes Risk score Model called Intelli-DRM estimate the severity of Diabetes mellitus.

This book serves as a reference book for scientific investigators who need to analyze disease data and/or numerical data, as well as researchers developing methodology in soft computing field. It may also be used as a textbook for a graduate and post graduate level course in machine learning or soft computing.

Table of Contents

Preface

Author Bio

Introduction

Literature Survey

Classifcation of Type-2 Diabetes using CVI based RBFN

Classifcation of Type-2 Diabetes using Spider Monkey Crisp Rule Miner

Classifcation of Type-2 Diabetes using Bat based Fuzzy Rule Miner

Classifcation of Type-2 Diabetes using Dual-Stage Cascade Network

Classifcation of Type-2 Diabetes using Bi-Level Ensemble Network

Intelli-DRM: An Intelligent Computational Model for Fore-casting Severity of Diabetes Mellitus

Conclusion and Future Research

Bibliography

About the Authors

Dr. Ramalingaswamy Cheruku is currently working as an Assistant Professor at Dr. Shyama Prasad Mukherjee International Institute of Information Technology, Naya Raipur, India. He has obtained Ph.D. in Computer Science and Engineering from National Institute of Technology Goa, India in 2018. He received B.Tech. degree in CSE from JNT University, Kakinada campus in 2008, M.Tech. degree in CSE from ABV-Indian Institute of Information Technology, Gwalior in 2011. He has served as developer in Tata Consultancy Services for 2 years. He has also published several papers in reputed journals and conferences.

Dr. Damodar Reddy Edla is an Assistant Professor in the department of Computer Science and Engineering at National Institute of Technology Goa, India. He received M.Sc. Degree from University of Hyderabad in 2006, M. Tech. in Computer Application and Ph. D. Degree in Computer Science and Engineering from Indian School of Mines Dhanbad in 2009 and 2013 respectively. His research interests include Cognitive Neuroscience, Data Mining, Wireless Sensor Networks and Brain Computer Interface. He has published more than 90 research articles in reputed journals and International conferences. He is senior member of IEEE and IACSIT. He is also Editorial Board member of several International journals.

Dr. Venkatanareshbabu Kuppili, Ph D (IIT Delhi), is with the Machine Learning Group, Department of CSE, NIT Goa, India, where he is currently an Assistant Professor. He was with Evalueserve pvt. ltd, as a Senior Research Associate. He is also actively involved in teaching and research development for the Graduate Program in Computer Science and Engineering Department at the NIT Goa. He has authored several research papers published in reputed International journals and conferences. He is senior member of IEEE.

Subject Categories

BISAC Subject Codes/Headings:
COM000000
COMPUTERS / General
COM012040
COMPUTERS / Programming / Games
COM014000
COMPUTERS / Computer Science
COM018000
COMPUTERS / Data Processing
COM021030
COMPUTERS / Database Management / Data Mining
COM032000
COMPUTERS / Information Technology
COM044000
COMPUTERS / Neural Networks
COM051240
COMPUTERS / Software Development & Engineering / Systems Analysis & Design
COM082000
COMPUTERS / Bioinformatics
MAT000000
MATHEMATICS / General
SCI008000
SCIENCE / Life Sciences / Biology / General