Applied Machine Learning in Healthcare by Takale, Dattatray G.; N Mahalle, Parikshit; Bere, Sachin S.;

Short description:

This book explores the latest advancements in machine learning techniques and their transformative applications in the healthcare domain. It delves into the use of machine learning for disease diagnosis and prognosis, showcasing its potential to enable accurate disease identification, effective risk stratification, and treatment planning. 

Long description:

This book explores the latest advancements in machine learning techniques and their transformative applications in the healthcare domain. It delves into the use of machine learning for disease diagnosis and prognosis, showcasing its potential to enable accurate disease identification, effective risk stratification, and personalised treatment planning. The role of machine learning in enhancing clinical decision support systems (CDSS) is examined in detail, with a focus on its impact on informed decision-making, predictive modeling, and real-time patient monitoring.

• Features real-world case studies and applications that demonstrate the practical use of machine learning in healthcare, including radiology, predictive analytics, personalised medicine, and resource optimisation.


• Covers essential stages of data preprocessing and feature engineering for healthcare datasets, addressing challenges such as data cleaning, normalisation, dimensionality reduction, and feature selection.


• Provides an in-depth overview of CDSS and the integration of machine learning algorithms to improve diagnostic accuracy and clinical workflow efficiency.


• Explores machine learning-driven real-time monitoring and alert systems, underscoring their utility in promptly identifying and responding to critical medical events.


• Discusses advances in medical image analysis, including segmentation, classification, and computer-aided diagnosis techniques.

This comprehensive volume serves as a valuable resource for researchers, clinicians, healthcare professionals, data scientists, and students seeking to understand and apply machine learning for improved healthcare outcomes.

Table of Contents:

1:  Ant Colony Optimization and Grey Wolf Optimization - A Comparative Study for Healthcare Resource Allocation During COVID-19 Across States in India 2: AI-Based Decision Support Systems for Personalized Maternal Health Management Before Pregnancy 3: Advances in Deep Neural Networks for Chronic Kidney Disease Diagnosis: A Systematic Review 4: Beyond Crystal Balls: Machine Learning's Role in Proactive Healthcare - Predicting and Preventing Disease Outcomes 5:  Unveiling the Veil: A Comprehensive Exploration of Interpretable Machine Learning for Healthcare and its Role in Elevating Transparency in Decision Support 6: A Comprehensive Exploration of How Deep Learning is Revolutionizing Patient Care in the Healthcare Landscape 7: Smart Healthcare Ecosystems: A Deep Dive into Applications, Advancements, and Ethical Considerations of Deep Learning Technologies 8: Healing Intelligence: A Deep Dive into the Cognitive Revolution of Healthcare through Advanced Deep Learning Technologies 9: Innovating at the Nexus: Unravelling the Impact of Deep Learning on Healthcare and Its Transformative Effect on Patient-Centric Solutions and Clinical Decision Support 10: Enhancing Healthcare Data Governance and Security: The Role of Adaptive Data Management Middleware in Federated Cloud Environments 11: Skin Cancer Detection Using U-Net 12: Revolutionizing Heart Disease Diagnosis using Machine Learning: A Case Study in Data-Driven Health care 13: Predicting drug response using Deep Learning techniques 14: Multimodal PCOS Detection: Combining XG Boost for Images with Zero Shot Learning for Textual Data 15:  Revolutionizing Healthcare: Leveraging Fine-Tuned Large Language Models for Personalized Question-Answering Chatbots 16:  Best Donor Selection for Liver Transplantation Using Artificial Neural Network and Machine Learning Algorithms 17: Clinical Decision Support Systems in Pre-Pregnancy Health: A Comparative Review of Traditional, Machine Learning, and Deep Learning Techniques 18: From Traditional Diagnostics to AI Innovations: A Comparative Study for Early Detection and Management of Chronic Kidney Disease 19:   Deep Learning in Medical Imaging for Intracranial Hemorrhage Detection and Segmentation 20: Enhancing Healthcare Resource Allocation: An Insights for Research