Enhancing Clinical Applicability: Interpretable Deep Learning for Cardiovascular Risk

Abstract

Heart disease is one of the major cause of death worldwide which emphasizes the critical need for sophisticated early-stage prediction that can assist clinicians in timely interventions. Traditional and convectional models fail to capture temporal time dependencies found in sequential health records and are unable to represent complicated nonlinear interactions in heterogeneous clinical data. This paper suggests an Attention enhanced Hybrid Deep Learning Model that combines both TabTransformer for contextual tabular feature data and Gated Recurrent Units for learning sequential patient data in order to overcome these constraints. To improve predicting sensitivity for high-risk patients, an attention mechanism is included to weigh the most crucial characteristics. Explainable AI frameworks like SHAP and Integrated Gradients are incorporated into the model to guarantee clinical applicability and transparent decision-making, allowing for both patient-specific reasoning and general feature attribution. Using stratified k means cross validation, the suggested architecture is assessed and contrasted with baseline models such as MLP, CNN, XGBOOST, and GRU to show its accuracy, precision, recall, area under curve, F1-score. According to experimental data, integrating multimodal learning and XAI-driven interpretability greatly improves heart attack prediction systems predictive power and credibility, allowing for practical clinical implementation.

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