CARDIORISK_ME: A MACHINE LEARNING-BASED PREDICTIVE ANALYTICS FOR HEART DISEASE
DOI:
https://doi.org/10.35631/JISTM.1144057Keywords:
Prediction, Machine Learning, Artificial Intelligence, Heart Care, CardiovascularAbstract
Heart disease remains one of the leading causes of death worldwide. Therefore, timely detection is essential to help improve patients' health. While machine learning and artificial intelligence have advanced significantly, implementation and development challenges remain. This study addresses these issues by providing a machine learning-based heart disease prediction model and presenting the results through an accessible, user-friendly web interface. In addition, this research analyzes and assesses clinical and physiological parameters relevant to heart disease risk prediction and develops a prediction system using Logistic Regression, Decision Tree, and Random Forest classifiers. This work supports efforts to increase awareness and control of heart disease, which continues to affect public health globally. In this study, a cardiac risk prediction system (CardioRisk_Me) was developed using a machine learning approach based on clinical and physiological attributes. The dataset is represented by 304 patient records from open-access repositories. The pre-processed data includes scaled features, encoded variables, cleaned extractions, and a training-testing split. Three machine learning algorithms were tested: Logistic Regression, Decision Tree, and Random Forest, using test-based measures of Accuracy, Precision, Recall, F1-Score, and ROC-AUC. Logistic Regression achieved the highest accuracy (86.27%) and the closest to perfect precision, Recall, F1-score, and ROC-AUC (90.00%, 87.10%, 88.52%, and 89.19%, respectively). The chosen model was incorporated into a web-based prototype built with Next.js and Django, creating a real time heart disease risk prediction system. Overall, the results illustrate the promise of machine learning in supporting risk assessment and preventive healthcare.
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References
Ahmarofi, A. A. (2022). An improved momentum rate in artificial neural networks for estimating product cycle time at semi-automatic production. In Human-centered technology for a better tomorrow: Proceedings of HUMENS 2021 (pp. 193–202). Springer Singapore. https://doi.org/10.1007/978-981-16-4115-2_15
Ahmarofi, A. A., Abidin, N. Z., & Zabidi, N. Z. (2022). Prediction of intensive care cases for COVID-19 pandemic in Malaysia: An artificial neural networks approach. ASM Science Journal, 16, 1–10. https://doi.org/10.32802/asmscj.2021.747
Ahmarofi, A. A., Gunny, A. A. N., Jamil, J. M., & Amlus, N. (2022). The optimization of the halophilic cellulase production: A 3-2-1 multilayer perceptron artificial neural network approach. In Symposium on Intelligent Manufacturing and Mechatronics (pp. 40–46). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-8954-3_5
Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., Hamid, R. A., Alamoodi, A. H., Albahri, O. S., Zaidan, B. B., Peh, S. C., Gu, X., Al-qaysi, Z. T., Garfan, S., Mohammed, K. I., Al-Bayati, B., Salih, M. M., Zaidan, A. A., Al-Obaidi, J. R., ... Deveci, M. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion, 96, 156–191. https://doi.org/10.1016/j.inffus.2023.03.008
Ali, F., El-Sappagh, S., Islam, S. R., Kwak, D., Ali, A., Imran, M., & Kwak, K. S. (2020). A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion, 63, 208–222. https://doi.org/10.1016/j.inffus.2020.06.008
Aliasgharzadeh, S., Yagin, N. L., Namazi, N., Haji, S., Alamdari, N. M., Pourmoradian, S., & Ghorbanverdi, Z. (2026). Depression severity and cardiovascular risk in patients with type 2 diabetes mellitus: Insights from the Framingham Risk Score. Scientific Reports, 1–8. https://doi.org/10.1038/s41598-026-43077-8
Bevan, G., Pandey, A., Griggs, S., Dalton, J. E., Zidar, D., Patel, S., Wright, J. T., Jr., Rajagopalan, S., & Al-Kindi, S. (2023). Neighborhood-level social vulnerability and prevalence of cardiovascular risk factors and coronary heart disease. Current Problems in Cardiology, 48(8), Article 101182. https://doi.org/10.1016/j.cpcardiol.2022.101182
Choudhury, A., Mondal, A., & Sarkar, S. (2024). Searches for the BSM scenarios at the LHC using decision tree-based machine learning algorithms: A comparative study and review of random forest, AdaBoost, XGBoost and LightGBM frameworks. The European Physical Journal Special Topics, 1–39. https://doi.org/10.1140/epjs/s11734-024-01308-x
Dogheim, G. M., & Hussain, A. (2023). Patient care through AI-driven remote monitoring: Analyzing the role of predictive models and intelligent alerts in preventive medicine. Journal of Contemporary Healthcare Analytics, 7(1), 94–110. https://api.semanticscholar.org/CorpusID:276021100
Gaziano, T. A. (2022). Cardiovascular diseases worldwide. In A public health approach to cardiovascular disease prevention and management (pp. 8–18). CRC Press. https://doi.org/10.1201/b23266
Ghaffar Nia, N., Kaplanoglu, E., & Nasab, A. (2023). Evaluation of artificial intelligence techniques in disease risk prediction and prediction. Discover Artificial Intelligence, 3(1), Article 5. https://doi.org/10.1007/s44163-023-00049-5
Gray, M. P., Berman, Y., Bottà, G., Grieve, S. M., Ho, A., Hu, J., Nelson, M. R., & Rogers, C. (2023). Incorporating a polygenic risk score-triaged coronary calcium score into cardiovascular disease examinations to identify subclinical coronary artery disease (ESCALATE): Protocol for a prospective, nonrandomized implementation trial. American Heart Journal, 264, 163–173. https://doi.org/10.1016/j.ahj.2023.06.012
Hughes, A., Shandhi, M. M. H., Master, H., Dunn, J., & Brittain, E. (2023). Wearable devices in cardiovascular medicine. Circulation Research, 132(5), 652–670. https://doi.org/10.1161/CIRCRESAHA.122.3223
Husnain, A., Saeed, A., Hussain, A., Ahmad, A., & Gondal, M. N. (2024). Harnessing AI for early detection of cardiovascular diseases: Insights from predictive models using patient data. International Journal for Multidisciplinary Research, 6(5), 1–12. https://doi.org/10.36948/ijfmr.2024.v06i05.27878
Karna, V. V. R., Karna, V. R., Janamala, V., Devana, V. K. R., Ch, V. R. S., & Tummala, A. B. (2024). A comprehensive review on heart disease risk prediction using machine learning and deep learning algorithms. Archives of Computational Methods in Engineering, 1–33. https://doi.org/10.1007/s11831-024-10194-4
Law, J. H., Sultan, N., & Finer, S. (2023). Advancing the communication of genetic risk for cardiometabolic diseases: A critical interpretive synthesis. BMC Medicine, 21, Article 432. https://doi.org/10.1186/s12916-023-03150-9
Li, C., Liu, X., Shen, P., Sun, Y., Zhou, T., Chen, W., Liu, Z., Wang, Z., & Gao, P. (2024). Improving cardiovascular risk prediction through machine learning modeling of irregularly repeated electronic health records. European Heart Journal – Digital Health, 5(1), 30–40. https://doi.org/10.1093/ehjdh/ztad058
Liu, W., Laranjo, L., Klimis, H., Chiang, J., Yue, J., Marschner, S., Thiagalingam, A., & Chow, C. K. (2023). Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: A systematic review and meta-analysis. European Heart Journal – Quality of Care and Clinical Outcomes, 9(4), 310–322. https://doi.org/10.1093/ehjqcco/qcad017
MacKay, C., Klement, W., Vanberkel, P., Lamond, N., Urquhart, R., & Rigby, M. (2023). A framework for implementing machine learning in healthcare based on the concepts of preconditions and postconditions. Healthcare Analytics, 3, Article 100155. https://doi.org/10.1016/j.health.2023.100155
Netala, V. R., Teertam, S. K., Li, H., & Zhang, Z. (2024). A comprehensive review of cardiovascular disease management: Cardiac biomarkers, imaging modalities, pharmacotherapy, surgical interventions, and herbal remedies. Cells, 13(17), Article 1471. https://doi.org/10.3390/cells13171471
Pakdil, F., Ebrahimpour, M., Yih, Y., Zhang, Z., & Viravan, B. (2026). Trends in 30-day readmission prediction: A systematic review and method–condition mapping of analytical approaches. Computers & Industrial Engineering, 220, Article 112230. https://doi.org/10.1016/j.cie.2026.112230
Pan, B., Hirota, K., Jia, Z., & Dai, Y. (2023). A review of multimodal emotion recognition from datasets, preprocessing, features, and fusion methods. Neurocomputing, 558, Article 126866. https://doi.org/10.1016/j.neucom.2023.126866
Qureshi, N. Q., Mufarrih, S. H., Bloomfield, G. S., Tariq, W., Almas, A., Mokdad, A. H., Bartlett, J., Nisar, I., Siddiqi, S., Bhutta, Z., Mark, D., Douglas, P. S., & Samad, Z. (2021). Disparities in cardiovascular research output and disease outcomes among high-, middle- and low-income countries: An analysis of global cardiovascular publications over the last decade (2008–2017). Global Heart, 16(1), Article 4. https://doi.org/10.5334/gh.815
Thomas, A., Ryan, C. P., Caspi, A., Liu, Z., Moffitt, T. E., Sugden, K., Williams, B. S., Belsky, D. W., & Gu, Y. (2024). Diet, pace of biological aging, and risk of dementia in the Framingham Heart Study. Annals of Neurology, 95(6), 1069–1079. https://doi.org/10.1002/ana.26900
Udegbe, F. C., Nwankwo, E. I., Igwama, G. T., & Olaboye, J. A. (2023). Real-time data integration in diagnostic devices for predictive modeling of infectious disease outbreaks. Computer Science & IT Research Journal, 4(3), 1–18. https://doi.org/10.51594/csitrj.v4i3.1502
Ullah, M., Hamayun, S., Wahab, A., Khan, S. U., Rehman, M. U., Haq, Z. U., Rahman, G., & Naeem, M. (2023). Smart technologies are used as smart tools in the management of cardiovascular disease and their future perspective. Current Problems in Cardiology, 48(11), Article 101922. https://doi.org/10.1016/j.cpcardiol.2023.101922
Vaduganathan, M., Mensah, G. A., Turco, J. V., Fuster, V., & Roth, G. A. (2022). The global burden of cardiovascular diseases and risk: A compass for future health. Journal of the American College of Cardiology, 80(25), 2361–2371. https://doi.org/10.1016/j.jacc.2022.11.005
Yang, H., Luo, Y. M., Ma, C. Y., Zhang, T. Y., Zhou, T., Ren, X. L., Zhang, J., & Lin, H. (2023). A gender-specific risk assessment of coronary heart disease based on physical examination data. NPJ Digital Medicine, 6(1), Article 136. https://doi.org/10.1038/s41746-023-00887-8
Zhang, L., Tong, Z., Han, R., Guo, R., Zang, S., Zhang, X., Li, X., & Yang, Y. (2023). Global, regional, and national burdens of ischemic heart disease attributable to smoking from 1990 to 2019. Journal of the American Heart Association, 12(3), Article e028193. https://doi.org/10.1161/JAHA.122.028193
