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Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion
arXiv – CS AI|Md. Tahsin Amin, Tanim Ahmmod, Zannatul Ferdus, Talukder Naemul Hasan Naem, Ehsanul Ferdous, Arpita Bhattacharjee, Ishmam Ahmed Solaiman, Nahiyan Bin Noor||6 views
🤖AI Summary
Researchers developed a hybrid system combining machine learning ensembles with large language models for heart disease prediction, achieving 96.62% accuracy. The study found that traditional ML models (95.78% accuracy) outperformed standalone LLMs (78.9% accuracy), but combining both approaches yielded the best results for clinical decision-support tools.
Key Takeaways
- →Machine learning ensembles achieved 95.78% accuracy with 0.96 ROC-AUC for heart disease prediction using 1,190 patient records.
- →Large language models performed moderately with 78.9% accuracy in zero-shot settings and 72.6% in few-shot scenarios.
- →A hybrid ML-LLM fusion system using Gemini 2.5 Flash achieved the highest performance at 96.62% accuracy with 0.97 AUC.
- →Traditional ML models like Random Forest, XGBoost, LightGBM, and CatBoost significantly outperformed LLMs for structured tabular data.
- →The hybrid approach shows promise for enhancing clinical decision-support systems by combining ML precision with LLM reasoning capabilities.
#machine-learning#llm#healthcare-ai#ensemble-models#hybrid-systems#medical-prediction#gemini#clinical-ai
Read Original →via arXiv – CS AI
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