🤖 AI Summary
This study addresses the limited reliability of single-model approaches in early cardiovascular disease prediction by proposing a hybrid architecture that integrates ensemble learning with large language models (LLMs). The framework combines mainstream ensemble methods—XGBoost, LightGBM, CatBoost, and Random Forest—with open-source LLMs such as Gemini 2.5 Flash through a voting mechanism, leveraging the OpenRouter API to enable zero-shot and few-shot inference. Evaluated under uncertainty, the proposed method achieves a prediction accuracy of 96.62% and an AUC of 0.97, substantially outperforming standalone ensemble models (95.78%) and LLMs alone (maximum 78.9%). This work presents the first empirical validation of an ML–LLM voting fusion strategy for clinical decision support, demonstrating its enhanced robustness and efficacy in real-world diagnostic scenarios.
📝 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.