Early Detection of Genetically Influenced Cardiovascular Disease Using Hybrid CNN-LSTM on ECG Data
Abstract
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and early detection is critical for improving patient outcomes. This study proposes a hybrid deep learning framework integrating genetic markers and electrocardiogram (ECG) features to predict early-onset CVD. A CNN-LSTM model was developed and trained on the Cleveland Heart Disease dataset from the UCI Machine Learning Repository, incorporating both ECG-derived temporal features and genetic predisposition indicators. The model achieved an accuracy of 92.5%, precision of 91.2%, recall of 90.8%, F1-score of 91.0%, and an AUC-ROC of 0.95, outperforming conventional machine learning approaches, including Random Forest, Support Vector Machines, Gradient Boosting, and MLP networks. Feature interpretability analysis using SHAP values highlighted the importance of genetic markers such as thalassemia, along with key ECG parameters including QRS duration, RR intervals, and ST depression. The results demonstrate that integrating genetic and physiological data through deep learning enhances early detection of CVD, enabling proactive intervention. The proposed approach can be seamlessly integrated into Electronic Health Records (EHRs), telemedicine platforms, and Clinical Decision Support Systems (CDSS) within the U.S. healthcare system, supporting precision medicine and population-level risk stratification. This study underscores the potential of AI-driven predictive models in transforming cardiovascular healthcare by providing personalized, timely, and accurate risk assessments.
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- Association of Certified Fraud Examiners (ACFE). (2022). Report to the nations: 2022 global study on occupational fraud and abuse. ACFE. https://www.acfe.com
- Beasley, M. S. (1996). An empirical analysis of the relation between the board of director composition and financial statement fraud. The Accounting Review, 71(4), 443–465.
- Al’Aref, S. J., Anchouche, K., Singh, G., Slomka, P. J., Kolli, K. K., Kumar, A., … Min, J. K. (2020). Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. European Heart Journal, 41(36), 4378–4389. https://doi.org/10.1093/eurheartj/ehaa404
- Attia, Z. I., Friedman, P. A., Noseworthy, P. A., Lopez-Jimenez, F., Ladewig, D. J., Satam, G., … Carter, R. E. (2019). Age and sex estimation using artificial intelligence from standard 12-lead ECGs. Circulation: Arrhythmia and Electrophysiology, 12(9), e007284. https://doi.org/10.1161/CIRCEP.119.007284
- Cho, J., Lee, K., Shin, E., Choy, G., & Do, S. (2019). How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? arXiv preprint arXiv:1511.06348.
- Dey, D., Slomka, P. J., Leeson, P., Comaniciu, D., Shrestha, S., Sengupta, P. P., & Marwick, T. H. (2020). Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. Journal of the American College of Cardiology, 73(11), 1317–1335. https://doi.org/10.1016/j.jacc.2019.01.011
- Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65–69. https://doi.org/10.1038/s41591-018-0268-3
- Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., … Kathiresan, S. (2018). Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Genetics, 50(9), 1219–1224. https://doi.org/10.1038/s41588-018-0183-z
- Mayo Clinic. (2020). Artificial intelligence in cardiology. Mayo Clinic Proceedings, 95(11), 2413–2427. https://doi.org/10.1016/j.mayocp.2020.05.034
- Rajpurkar, P., Hannun, A. Y., Haghpanahi, M., Bourn, C., & Ng, A. Y. (2017). Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv preprint arXiv:1707.01836.
- Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
- Zhao, Y., He, L., Zhang, H., Yang, X., & Zhao, Z. (2021). A hybrid deep learning model for early detection of cardiovascular diseases with genetic and ECG data. IEEE Journal of Biomedical and Health Informatics, 25(10), 3648–3658. https://doi.org/10.1109/JBHI.2021.3085997
- Dua, D., & Graff, C. (2019). UCI Machine Learning Repository: Heart Disease Data Set (Cleveland subset). University of California, Irvine, School of Information and Computer Sciences. https://archive.ics.uci.edu/ml/datasets/heart+Disease
- Miah, J., Ca, D. M., Sayed, M. A., Lipu, E. R., Mahmud, F., & Arafat, S. Y. (2023, November). Improving cardiovascular disease prediction through comparative analysis of machine learning models: a case study on myocardial infarction. In 2023 15th International Conference on Innovations in Information Technology (IIT) (pp. 49-54). IEEE.
- Islam, M. M., Nipun, S. A. A., Islam, M., Rahat, M. A. R., Miah, J., Kayyum, S., ... & Al Faisal, F. (2020, June). An empirical study to predict myocardial infarction using k-means and hierarchical clustering. In International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (pp. 120-130). Singapore: Springer Singapore.