Introduction

Healthcare analytics indeed plays a crucial role in leveraging data from various sources to identify trends, patterns, and insights that can lead to improvements in healthcare delivery and decision-making. Feature selection is particularly important in healthcare analytics because it helps identify the most relevant data attributes or features that contribute to predictive models or analysis. By selecting the most informative features, healthcare professionals can build more accurate models and gain better insights into patient outcomes, treatment effectiveness, disease prediction, and more. Challenges in healthcare data include issues related to data quality, privacy concerns, data integration from disparate sources, and the complexity of healthcare systems. Overcoming these challenges requires robust analytics techniques and methodologies tailored to the healthcare domain. Machine learning algorithms play a significant role in healthcare analytics by enabling predictive modeling, clustering, classification, and other tasks. Choosing the right algorithm depends on the specific healthcare application and the nature of the data being analyzed. This chapter outlines Feature Selection algorithms and discusses the challenges associated with healthcare data. It also introduces an abstract architecture for data analytics in the healthcare domain. Furthermore, it compares and categorizes various machine learning algorithms and techniques according to their applications in healthcare analytics.