Unsupervised machine learning for cardiovascular disease: A framework for future studies.

Area of Science:

• Cardiology
• Biomedical Informatics
• Data Science

Background:

• Cardiovascular diseases (CVDs) management requires precise patient stratification.
• Unsupervised machine learning (ML) offers potential for identifying patient subgroups based on clinical data patterns.
• Current clinical application of clustering methods for CVDs is hindered by a lack of accessible tools and validation.

Purpose of the Study:

• To present a systematic framework for applying unsupervised ML clustering techniques in cardiovascular disease research.
• To guide the development of robust, externally validated models for clinical integration.
• To enhance the utility of clustering for improved CVD risk stratification and personalized treatment.

Main Methods:

• Review of unsupervised machine learning techniques, specifically clustering algorithms.
• Development of a stepwise framework for patient cluster identification and validation.
• Integration of outcome associations and predictive modeling for clinical application.

Main Results:

• A systematic framework for applying unsupervised ML to CVD research is proposed.
• The framework facilitates identification of distinct patient clusters with varying prognoses.
• It supports the development of externally validated predictive models for clinical use.

Conclusions:

• Unsupervised ML clustering can significantly improve cardiovascular disease patient characterization and stratification.
• The proposed framework provides a roadmap for developing clinically applicable, validated models.
• This approach promises to enhance risk stratification and personalize treatment strategies for CVD patients.