Article

An Explainable Self-Supervised Learning Framework for Interpretable and Accurate Heart Disease Prediction Using EDA–SimCLR–SHAP Pipeline

2025-10-22

Abstract excerpt

<title>Abstract</title> <p>Precise and interpretable risk prediction for heart disease is a core challenge of contemporary cardiovascular medicine, with early intervention lowering mortality and treatment cost significantly. Conventional supervised learning models lack generalizability on small heterogeneous clinical data sets and cannot offer understandable explanations for their predictions. To face these chall...

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Literature Corpus work: 5c78c85e-8218-5e5a-a0c8-8dbb2ba31a11
DOI: 10.21203/rs.3.rs-7890495/v1

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