AI Research
Evaluating LLM-Generated Rules for Heart Disease Prediction
Bridging the transparency gap in medical diagnostics using LLM-driven rule induction.
This study compares traditional machine learning models and Large Language Model (LLM)-generated rule-based systems for heart disease prediction using the UCI Heart Disease dataset. Several classifiers, including Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Mach...
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ArXiv CS.LG
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2 min
ArXiv CS.LG
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Generated insight by OnAirToday AI
Leveraging LLMs to generate interpretable, rule-based diagnostic frameworks bridges the gap between complex black-box models and medical transparency. This approach enhances clinician trust by providing explainable logic for heart disease risk assessments directly derived from clinical datasets.
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