Evaluating LLM-Generated Rules for Heart Disease Prediction

Understand

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 Machine (SVM), Naive Bayes, Decision Tree, and Random Forest, were evaluated alongside rule-based systems generated using GPT-4o and Claude Sonnet 4.6.
• Model performance was assessed using accuracy, precision, recall, and F1-score metrics.
• Experimental results show that traditional machine learning models consistently outperform LLM-generated rule-based systems in predictive performance.