Open Access

ARTICLE

A Deep Learning Framework for Heart Disease Prediction with Explainable Artificial Intelligence

Muhammad Adil¹, Nadeem Javaid^1,*, Imran Ahmed², Abrar Ahmed³, Nabil Alrajeh^4,*

1 International Graduate School of AI, National Yunlin University of Science and Technology, Douliu, 64002, Taiwan
2 School of Computing and Information Science, Anglia Ruskin University, Cambridge, CB11PT, UK
3 Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, 44000, Pakistan
4 Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh, 11633, Saudi Arabia

* Corresponding Authors: Nadeem Javaid. Email: ; Nabil Alrajeh. Email:

Computers, Materials & Continua 2026, 86(1), 1-20. https://doi.org/10.32604/cmc.2025.071215

Received 02 August 2025; Accepted 10 September 2025; Issue published 10 November 2025

Abstract

Heart disease remains a leading cause of mortality worldwide, emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention. However, existing Deep Learning (DL) approaches often face several limitations, including inefficient feature extraction, class imbalance, suboptimal classification performance, and limited interpretability, which collectively hinder their deployment in clinical settings. To address these challenges, we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture. The preprocessing stage involves label encoding and feature scaling. To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset, the localized random affine shadowsampling technique is employed, which enhances minority class representation while minimizing overfitting. At the core of the framework lies the Deep Residual Network (DeepResNet), which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex, non-linear relationships in the data. Experimental results demonstrate that the proposed model significantly outperforms existing techniques, achieving improvements of 3.26% in accuracy, 3.16% in area under the receiver operating characteristics, 1.09% in recall, and 1.07% in F1-score. Furthermore, robustness is validated using 10-fold cross-validation, confirming the model’s generalizability across diverse data distributions. Moreover, model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations, offering valuable insights into the contribution of individual features to model predictions. Overall, the proposed DL framework presents a robust, interpretable, and clinically applicable solution for heart disease prediction.

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Keywords

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