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.
Keywords: Big data, Data analytics, Electronic Health Record (EHR), Healthcare analytics, Machine learning algorithms.
