Computational Intelligence and Healthcare Informatics

  • 7h 23m
  • Ahmed A. Elngar, Alok Ranjan Tripathy, Om Prakash Jena, Zdzislaw Polkowski
  • John Wiley & Sons (US)
  • 2021


The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis.

Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments.

This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis.


The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.

About the Author

Om Prakash Jena PhD is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. He has more than 30 research articles in peer-reviewed journals and 4 patents.

Alok Ranjan Tripathy PhD is an assistant professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India.

Ahmed A. Elngar PhD is an assistant professor of Computer Science, Chair of Scientific Innovation Research Group (SIRG), Director of Technological and Informatics Studies Center, at Beni-Suef University, Egypt.

Zdzislaw Polkowski PhD is Professor in the Faculty of Technical Sciences, Jan Wyzykowski University, Polkowice, Poland. He has published more than 75 research articles in peer-reviewed journals.

In this Book

  • Machine Learning and Big Data—An Approach toward Better Healthcare Services
  • Thoracic Image Analysis Using Deep Learning
  • Feature Selection and Machine Learning Models for High-Dimensional Data—State-of-the-Art
  • A Smart Web Application for Symptom-Based Disease Detection and Prediction Using State-of-the-Art ML and ANN Models
  • Classification of Heart Sound Signals Using Time-Frequency Image Texture Features
  • Improving Multi-Label Classification in Prototype Selection Scenario
  • A Machine Learning–Based Intelligent Computational Framework for the Prediction of Diabetes Disease
  • Hyperparameter Tuning of Ensemble Classifiers Using Grid Search and Random Search for Prediction of Heart Disease
  • Computational Intelligence and Healthcare Informatics Part III—Recent Development and Advanced Methodologies
  • Wolfram’s Cellular Automata Model in Health Informatics
  • COVID-19—Classification of Countries for Analysis and Prediction of Global Novel Corona Virus Infections Disease Using Data Mining Techniques
  • Sentiment Analysis on Social Media for Emotional Prediction during COVID-19 Pandemic Using Efficient Machine Learning Approach
  • Primary Healthcare Model for Remote Area Using Self-Organizing Map Network
  • Face Mask Detection in Real-Time Video Stream Using Deep Learning
  • A Computational Intelligence Approach for Skin Disease Identification Using Machine/Deep Learning Algorithms
  • Asymptotic Patients’ Healthcare Monitoring and Identification of Health Ailments in Post COVID-19 Scenario
  • COVID-19 Detection System Using Cellular Automata–Based Segmentation Techniques
  • Interesting Patterns from COVID-19 Dataset Using Graph-Based Statistical Analysis for Preventive Measures
  • Conceptualizing Tomorrow’s Healthcare Through Digitization
  • Domain Adaptation of Parts of Speech Annotators in Hindi Biomedical Corpus—An NLP Approach
  • Application of Natural Language Processing in Healthcare