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<p>Accurate predictive models are crucial for early detection and intervention of Heart Disease (HD), which continues to be a major cause of death worldwide. However, the challenges of high dimensionality and data imbalance affect the precision and generalizability of predictions. We propose a novel deep model, the Fully Connected Wave Network (FCW-Net), for early HD prediction. To mitigate class imbalance, Proximity Weighted Synthetic (ProWSyn) oversampling technique is employed, while Principal Component Analysis (PCA) is used to reduce dimensionality, enhancing model efficiency and prediction accuracy. With an AUC-ROC of 0.9622, an accuracy of 0.9237, a precision of 0.8856, an F1-score of 0.9268, and a recall of 0.9721, our results demonstrate that the FCW-Net deep model achieves superior performance than baseline models when using PCA and ProWSyn. Model transparency is further improved by eXplainable Artificial Intelligence technique, SHapley Additive exPlanations analysis, which provides insights into feature contributions to HD risk.</p>

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An explainable AI based new deep learning solution for efficient heart disease prediction at early stages

  • 期刊论文

  • 发表日期：

    2025年2月6日

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  • Muhammad Talha Ashfaq,
  • Nadeem Javaid,
  • Nabil Alrajeh,
  • Syed Saqib Ali

摘要

Accurate predictive models are crucial for early detection and intervention of Heart Disease (HD), which continues to be a major cause of death worldwide. However, the challenges of high dimensionality and data imbalance affect the precision and generalizability of predictions. We propose a novel deep model, the Fully Connected Wave Network (FCW-Net), for early HD prediction. To mitigate class imbalance, Proximity Weighted Synthetic (ProWSyn) oversampling technique is employed, while Principal Component Analysis (PCA) is used to reduce dimensionality, enhancing model efficiency and prediction accuracy. With an AUC-ROC of 0.9622, an accuracy of 0.9237, a precision of 0.8856, an F1-score of 0.9268, and a recall of 0.9721, our results demonstrate that the FCW-Net deep model achieves superior performance than baseline models when using PCA and ProWSyn. Model transparency is further improved by eXplainable Artificial Intelligence technique, SHapley Additive exPlanations analysis, which provides insights into feature contributions to HD risk.