Related papers: Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion

Integrating Machine Learning Ensembles and Large Language Models for Heart Disease Prediction Using Voting Fusion

URL: http://arxiv.org/abs/2602.22280v1
Date: Wed, 25 Feb 2026 10:53:57 GMT
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,
Abstract summary: This research predicts cardiovascular disease using a merged dataset of 1,190 patient records.<n>A hybrid fusion of the ML ensemble and reasoning under Gemini 2.5 Flash achieved the best results.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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 % accuracy) work best when combined with ML models rather than used alone. Results show that ML ensembles achieved the highest performance (95.78% accuracy, ROC-AUC 0.96), while LLMs performed moderately in zero-shot (78.9%) and slightly better in few-shot (72.6%) settings. The proposed hybrid method enhanced the strength in uncertain situations, illustrating that ensemble ML is considered the best structured tabular prediction case, but it can be integrated with hybrid ML-LLM systems to provide a minor increase and open the way to more reliable clinical decision-support tools.

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