Applied Machine Learning in Healthcare

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.