Efficient diagnosis of cardiovascular disease using composite deep learning and explainable AI technique

Qaisrani S.N., Khattak A., Zubair Asghar M., Kuleev R., Imbugva G.
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During the last several decades, cardiovascular disease has surpassed all others as the leading cause of mortality in both high-income and low-income countries. The mortality rate from heart disorders may be lowered with early identification and close clinical monitoring. However, it is not feasible to adequately monitor patients every day, and 24-hour consultation with a doctor is not a feasible option, since it requires more sagacity, time, and knowledge than is currently available.

In this study, we examine the Explainable Artificial Intelligence (XAI) technique, namely, the SHAP interpretability approach, in order to educate the medical professionals about the Explainable AI (XAI) methods that can be helpful in healthcare. The XAI methods enhance the trust and understandability of both practitioners and Health Researchers in AI Models. In this work, we propose a composite Deep Learning model: Bi-LSTM+CNN model to effectively predict heart disease from patient data. After balancing the dataset, the Bi-LSTM+CNN model was used. In contrast to other studies, our proposed hybrid deep learning model produced excellent experimental results, including 99.05% accuracy, 99% precision, 99% recall, and 99% F1-score.

Keywords: explainable AI, cross-validation, backward elimination, REFCV, cardiovascular disease, healthcare
Citation in English: Qaisrani S.N., Khattak A., Zubair Asghar M., Kuleev R., Imbugva G. Efficient diagnosis of cardiovascular disease using composite deep learning and explainable AI technique // Computer Research and Modeling, 2024, vol. 16, no. 7, pp. 1651-1666
Citation in English: Qaisrani S.N., Khattak A., Zubair Asghar M., Kuleev R., Imbugva G. Efficient diagnosis of cardiovascular disease using composite deep learning and explainable AI technique // Computer Research and Modeling, 2024, vol. 16, no. 7, pp. 1651-1666
DOI: 10.20537/2076-7633-2024-16-7-1651-1666
URL: http://crm-en.ics.org.ru/journal/article/3560/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

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