Unsupervised Machine Learning Methods in Healthcare Informatics and Healthcare Analytics

Abstract

The healthcare domain generates vast amounts of complex data, which holds the potential to significantly improve patient care, operational efficiency, and decision-making. However, much of this data remains underutilized due to its unstructured nature and the lack of labeled datasets. Unsupervised machine learning methods have emerged as powerful tools to extract meaningful patterns and insights from such data, enabling advancements in healthcare informatics and analytics. This paper explores the application of unsupervised machine learning techniques, such as clustering, dimensionality reduction, and anomaly detection, in healthcare settings. By reviewing recent literature and proposing a robust methodology, this research demonstrates the impact of unsupervised learning on tasks like patient segmentation, disease risk stratification, and anomaly detection in medical data. Through experiments on synthetic and real-world datasets, we present results that highlight the efficacy of these methods. The discussion outlines the challenges, potential ethical implications, and future research directions. This paper aims to bridge the gap between theory and practical applications of unsupervised machine learning in healthcare.

DOI - https://doi.org/10.65525/SVUP.9788199963351.2026.165-171