Unsupervised machine learning methods and emerging applications in healthcare

Abstract

Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensional data and can help simplify complex datasets. This article provides an overview of key unsupervised machine learning techniques including K-means clustering, hierarchical clustering, principal component analysis, and factor analysis. With a deeper understanding of these analytical tools, unsupervised machine learning methods can be incorporated into health sciences research to identify novel risk factors, improve prevention strategies, and facilitate delivery of personalized therapies and targeted patient care.Level of evidence: I.

Keywords: Analytics; Artificial intelligence; Computational models; Editorial; Machine learning.