Hybrid AI Framework for the Early Detection of Heart Failure: Integrating Traditional Machine Learning and Generative Language Models With Clinical Data

Abstract

Cardiovascular disease (CVD) remains the leading cause of mortality globally, necessitating innovative approaches for early detection and risk stratification. This study introduces a hybrid artificial intelligence (AI) model that synergistically combines Convolutional Neural Networks (CNNs) and Large Language Models (LLMs) to enhance the accuracy of heart failure (HF) prediction. The CNN component effectively captures spatial patterns from structured clinical data, while the LLM component interprets complex, unstructured information, enabling a comprehensive analysis of patient health records. Our hybrid model achieved a superior accuracy of 95.1%, outperforming standalone models and demonstrating high precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) metrics. Key predictive features (risk factors, symptoms, signs, and electrocardiogram (ECG) investigations) identified include Chest Pain Type, Maximum Heart Rate (maxHR), and Exercise-Induced Angina, aligning with established clinical indicators of cardiac risk. Integrating explainable AI (XAI) techniques, such as Shapley Additive exPlanations (SHAP), provides transparency into the model's decision-making process, fostering trust and facilitating clinical adoption. These findings underscore the potential of hybrid AI models to revolutionize cardiovascular diagnostics by providing accurate, interpretable, and clinically relevant predictions, thereby supporting healthcare professionals in making informed decisions and improving patient outcomes.

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