Applied Intelligence for Healthcare Informatics

Techniques and Applications

Nazmul Siddique, Mohammad Shamsul Arefin, Md Zahid Hasan, M Shamim Kaiser

• Publisher: CRC Press
• Year: 2025
• eBook ISBN: 9781040425275
• Topic: Business
• Subtopic: Business Intelligence
• Index: Business

AI is revolutionizing healthcare, transforming diagnostics, treatment, and patient care. Applied Intelligence for Healthcare Informatics: Techniques and Applications explores cutting-edge AI solutions for disease prediction, medical imaging, mental health analysis, and healthcare decision-making.

Covering topics like heart disease prediction, COVID-19 detection, breast cancer classification, and ethical AI challenges, this book bridges theory with real-world applications. A must-read for researchers, practitioners, and innovators, it offers insights into the future of AI-driven healthcare.

Table of contents

• Cover Page
• Half-Title Page
• Title Page
• Copyright Page
• Dedication Page
• Contents
• Preface
• Contributors
• Editors
• Chapter 1 Explainable AI-Based Heart Attack Prediction Model Using Various Machine Learning and Ensemble Learning Approaches
• Chapter 2 Predicting Heart Block and Its Type Using Ensemble-Based Machine Learning
• Chapter 3 Towards COVID-19 Detection Information System Based on AIoT
• Chapter 4 Tomato Leaf Disease Detection Using Convolutional Neural Network
• Chapter 5 An Automatic Recognition System for Thyroid Nodule in Ultrasound Images Using Convolutional Neural Network
• Chapter 6 Malnutrition Prediction among Under-Five Children Using Machine Learning Techniques
• Chapter 7 Prediction of Depression Severity via Feature Grouping and Machine Learning with Burn Depression Checklist
• Chapter 8 Comparative Analysis of Machine Learning Methods for Symptoms-Based COVID-19 Detection
• Chapter 9 BCNet-11: A Dilated Convolutional Neural Network for Breast Cancer Classification Using Histopathology Images
• Chapter 10 Age Estimation from Human Facial Expression Using Deep Neural Network
• Chapter 11 Detecting Depression from Social Media Posts Using Comprehensive Machine Learning Approaches with TF-IDF and N-Grams
• Chapter 12 An Ensemble Approach and Comparison of LSTM, Bi-LSTM, and GNN Models to Predict Protein Secondary Structure
• Chapter 13 Psychological Stress Prediction of Human Using Machine Learning Algorithms
• Chapter 14 Face Mask Detection Using CNN: Ensuring Safety During COVID-19
• Chapter 15 Approximating the Survival Possibility of Post Thoracic Surgery for Lung Cancer Using Machine Learning
• Chapter 16 Time-Frequency Analysis Using fNIRS Signal for Pain Detection on Hemodynamic Response
• Chapter 17 A Study on Different SVM Kernels with Suitable Pre-Processing Technique and Parameter Optimization for Cardiovascular Disease Prediction
• Index