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[2602.22280] Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion
Summary
This research paper explores the integration of machine learning ensembles and large language models for predicting heart disease, demonstrating improved accuracy through a hybrid approach.
Why It Matters
Cardiovascular disease remains a leading cause of death worldwide, highlighting the need for effective predictive models. This study combines traditional machine learning with advanced language models, potentially enhancing clinical decision-making and patient outcomes.
Key Takeaways
- Hybrid models combining ML ensembles and LLMs can enhance heart disease prediction accuracy.
- Traditional ML models outperformed LLMs when used alone, achieving 95.78% accuracy.
- The proposed voting fusion method achieved the highest accuracy of 96.62% in predictions.
- LLMs showed moderate performance, indicating their effectiveness increases when integrated with ML models.
- This research opens avenues for more reliable clinical decision-support tools.
Computer Science > Machine Learning arXiv:2602.22280 (cs) [Submitted on 25 Feb 2026] Title:Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion Authors:Md. Tahsin Amin, Tanim Ahmmod, Zannatul Ferdus, Talukder Naemul Hasan Naem, Ehsanul Ferdous, Arpita Bhattacharjee, Ishmam Ahmed Solaiman, Nahiyan Bin Noor View a PDF of the paper titled Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion, by Md. Tahsin Amin and 7 other authors View PDF HTML (experimental) Abstract:Cardiovascular disease is the primary cause of death globally, necessitating early identification, precise risk classification, and dependable decision-support technologies. The advent of large language models (LLMs) provides new zero-shot and few-shot reasoning capabilities, even though machine learning (ML) algorithms, especially ensemble approaches like Random Forest, XGBoost, LightGBM, and CatBoost, are excellent at modeling complex, non-linear patient data and routinely beat logistic regression. This research predicts cardiovascular disease using a merged dataset of 1,190 patient records, comparing traditional machine learning models (95.78% accuracy, ROC-AUC 0.96) with open-source large language models via OpenRouter APIs. Finally, a hybrid fusion of the ML ensemble and LLM reasoning under Gemini 2.5 Flash achieved the best results (96.62% accuracy, 0.97 AUC), showing that LLMs (78.9 % a...
