Unsupervised machine learning methods and emerging applications in healthcare

Area of Science:

• Health Sciences
• Data Science
• Computational Biology

Background:

• High-dimensional data presents challenges in analysis and interpretation.
• Unsupervised machine learning (UML) offers powerful tools for uncovering latent patterns.
• UML can simplify complex datasets, revealing underlying structures.

Purpose of the Study:

• To provide an overview of key unsupervised machine learning techniques.
• To highlight the utility of UML in health sciences research.
• To demonstrate how UML can enhance risk factor identification and personalized medicine.

Main Methods:

• Overview of K-means clustering.
• Explanation of hierarchical clustering.
• Introduction to principal component analysis (PCA) and factor analysis.

Main Results:

• UML techniques effectively identify patterns and structures in high-dimensional data.
• These methods can simplify complex datasets for easier interpretation.
• Specific techniques like K-means, hierarchical clustering, PCA, and factor analysis are detailed.

Conclusions:

• Unsupervised machine learning is a valuable analytical tool for health sciences.
• UML can facilitate the identification of novel risk factors.
• Incorporating UML can improve prevention strategies and personalize patient therapies.