Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning, 1st Edition

  • Geeta Rani
  • Pradeep Kumar Tiwari
  • Published By:
  • ISBN-10: 1799827437
  • ISBN-13: 9781799827436
  • DDC: 616.0072
  • 586 Pages | eBook
  • Original Copyright 2021 | Published/Released January 2021
  • This publication's content originally published in print form: 2021

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By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. This book is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Advances in Medical Diagnosis, Treatment, and Care (AMDTC) Book Series.
Editorial Advisory Board.
List of Contributors.
Table of Contents.
Detailed Table of Contents.
1: Glaucoma Detection Using Convolutional Neural Networks.
2: Pre-Processing Highly Sparse and Frequently Evolving Standardized Electronic Health Records for Mining.
3: Image Classification Techniques.
4: Prediction Models.
5: Prediction Models for Healthcare Using Machine Learning: A Review.
6: Chronic Kidney Disease Prediction Using Data Mining Algorithms.
7: A Machine Learning Approach to Prevent Cancer.
8: Machine Learning Perspective in Cancer Research.
9: A Pathway to Differential Modelling of Nipah Virus.
10: Application of AI for Computer-Aided Diagnosis System to Detect Brain Tumors.
11: Application of Machine Learning to Analyse Biomedical Signals for Medical Diagnosis.
12: Artificial Bee Colony-Based Associative Classifier for Healthcare Data Diagnosis.
13: Artificial Intelligence Approaches to Detect Neurodegenerative Disease From Medical Records: A Perspective.
14: Clinical Decision Support Systems: Decision-Making System for Clinical Data.
15: Diagnosis and Prognosis of Ultrasound Fetal Growth Analysis Using Neuro-Fuzzy Based on Genetic Algorithms.
16: ECG Image Classification Using Deep Learning Approach.
17: Genetic Data Analysis.
18: Heart Disease Prediction Using Machine Learning.
19: Heuristic Approach Performances for Artificial Neural Networks Training.
20: Mental Health Through Biofeedback Is Important to Analyze: An App and Analysis.
21: Pre-Clustering Techniques for Healthcare System: Evaluation Measures, Evaluation Metrics, Comparative Study of Existing vs. Proposed Approaches.
22: Strategic Analysis in Prediction of Liver Disease Using Different Classification Algorithms.
23: Texture Segmentation and Features of Medical Images.
24: Towards Integrating Data Mining With Knowledge-Based System for Diagnosis of Human Eye Diseases: The Case of an African Hospital.
25: Use of IoT and Different Biofeedback to Measure TTH: An Approach for Healthcare 4.0.
26: ACO_NB-Based Hybrid Prediction Model for Medical Disease Diagnosis.
Compilation of References.
About the Contributors.