Machine Learning for Healthcare Informatics

This book explores how advanced machine learning techniques are transforming healthcare, highlighting innovative applications in disease diagnosis, treatment, and healthcare management. It shows that adaptation of machine learning can bring significant benefits for the sustainability of healthcare informatics in the era of 4.0 IR. With contributions from researchers and field experts, the book covers key topics such as predictive analytics, medical image processing, and personalized healthcare. Each chapter provides detailed methodologies, datasets, and experimental results, with practical insights into AI-driven diagnostics, patient monitoring, and decision-support systems. Designed for those seeking to apply machine learning in healthcare and to advance healthcare informatics, this book is a valuable resource for researchers, professionals, and students.

Nazmul Siddique is a researcher at the School of Computing, Engineering, and Intelligent Systems, Ulster University. He has published over 170 research papers and several books on cybernetics and computational intelligence. His editorial roles in top journals highlight his academic influence and contributions. Mohammad Shamsul Arefin is a professor at the Department of CSE, CUET, and Dean of Electrical and Computer Engineering. He has over 170 publications in journals and conferences on data mining, distributed computing, and machine learning. His leadership has significantly fostered research growth and academic excellence in many aspects. Mohammad Abu Yousuf is currently the Vice-Chancellor of Bangabandhu Sheikh Mujibur Rahman Digital University, Bangladesh, and a Professor at the Institute of Information Technology, Jahangirnagar University. He holds a B.Sc. in Computer Science and Engineering from Shahjalal University of Science and Technology, an M.Eng. in Biomedical Engineering from Kyung Hee University, South Korea, and a Ph.D. in Science and Engineering from Saitama University, Japan. With over 125 publications in peer-reviewed journals, conferences, and book chapters, his research spans Medical Image Processing, Human-Robot Interaction, Computer Vision, and Natural Language Processing. M. Shamim Kaiser is a professor and Chairman at the Institute of Information Technology, Jahangirnagar University. He has authored over 100 research papers on machine learning, cyber security, and cognitive radio networks. His leadership at IIT has driven academic and research excellence in ICT.

1. A Robust Deep Learning Based Hybrid Model to Detect Covid-19 Using Chest X-ray 2. Transfer learning-based approach to detect crop disease using android application 3. A Proposed Sequential Network Analysis for Identification of Hub Genes for Therapeutics in Tuberculosis and Its Overlaying Non-Communicable Disorders 4. Transfer-learning-based Feature Extractor Performance Analysis to Classify Black Gram Leaf Disease 5. Early Prediction of Breast Cancer using Deep Learning Models 6. Chest-InfNet: A Deep Learning Architecture for Lung Diseases Detection and Infected Region Localization from Chest X-Ray Images 7. Ensemble-Based Transfer Learning Approach for Brain Tumor Segmentation from MRI Images 8. Preventing Skin Cancer through Improved Skin Lesion Recognition: An Attention-Triplet and Multi-Layer Ensemble Based CNN Approach 9. Gastrointestinal Disease Classification through Explainable and Cost-Sensitive Deep Neural Networks with Supervised Contrastive Learning 10. COVID-19 Distance Learning Understanding Classification using Scalogram Based on Transfer Learning and Principal Feature Classifier from EEG Signals 11. Large Ensemble of Transfer-Learned Models for Plant Disease Recognition from Diverse Leaf Images 12. Computer-Aided Strategy to Diagnose Lung Cancer from CTScan Images Using Inception Architecture 13. Automated Bone Age Assessment using Deep Learning with Attention Module 14. Towards Bengali Health Text Identification using Deep Learning Technique 15. Brain Tumor Detection Using Fine-Tuned ResNet-101 on Magnetic Resonance Images 16. Automated Agricultural Pests Identification using Convolutional Neural Network-based Transfer Learning 17. CTFCP: A Cloud-based Deep Transfer Learning Framework for Analyzing Chest X-Ray Images to Detect Pneumonia