Open Access

A Machine Learning Framework for Predicting Cardiovascular Disease Risk: A Comparative Analysis Using the UCI Heart Disease Dataset

PDF

Dr. Elias R. Vance ¹ , Prof. Seraphina J. Choi ¹ ,

⁴ Department of Biomedical Informatics, King's College London, London, UK

⁴ School of Computer Science, National University of Singapore, Singapore

Abstract

Background: Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality worldwide. Traditional risk assessment methods often lack the predictive power needed for early and effective intervention. This study explores the potential of a machine learning-based framework to enhance the accuracy of CVD risk prediction.

Methods: We developed a specialized framework utilizing supervised learning algorithms to predict heart disease severity. The study leveraged the publicly available UCI Heart Disease Dataset, which contains 14 clinical and demographic attributes. We preprocessed the data and applied feature selection techniques before training and evaluating four models: Logistic Regression, Decision Trees, Random Forests, and XGBoost. The performance of each model was rigorously evaluated using standard metrics, including accuracy, precision, recall, and F1 score.

Results: A comparative analysis revealed that XGBoost consistently demonstrated superior performance among the tested algorithms. The XGBoost model achieved the highest accuracy, at 62.5%, indicating its strong capability in identifying at-risk patients. The other models showed varied performance, underscoring the importance of model selection for this task.

Discussion: The findings confirm that machine learning, and specifically the XGBoost algorithm, can effectively analyze complex clinical data to predict cardiovascular disease risk. This framework holds promise as a powerful clinical decision-support tool. Future work should focus on validating the framework with larger datasets and exploring its integration into clinical practice.

Keywords

Cardiovascular Disease, Machine Learning, Predictive Analytics

References

Dey, S., et al. (2018). Predicting risk of cardiovascular diseases using machine learning algorithms. Computers in Biology and Medicine.

Dinh, A., et al. (2019). A deep learning system for detecting diabetic retinopathy and cardiovascular risk factors from retinal fundus images. Nature Biomedical Engineering.

Krittanawong, C., et al. (2017). Machine learning for cardiovascular disease prediction. Journal of the American College of Cardiology.

Shah, S. J., et al. (2019). Artificial intelligence and machine learning in cardiology. JACC: Heart Failure.

Ahmad, M. A., et al. (2018). Interpretable machine learning in healthcare. In Proceedings of the 23rd ACM SIGKDD, 2018.

Amin, M. S., et al. (2019). Comparative analysis of machine learning algorithms for heart disease prediction. SN Applied Sciences.

Chaurasia, V., & Pal, S. (2014). A novel approach for heart disease prediction using data mining and soft computing techniques. International Journal of Computer Science and Information Technology.

Uddin, S., et al. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making.

Khan, N., et al. (2020). Feature selection and classification in high-dimensional biomedical data. Journal of Biomedical Informatics.

Saeys, Y., et al. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics.

Alizadehsani, R., et al. (2018). Machine learning-based coronary artery disease diagnosis: A review. Computers in Biology and Medicine.

Janosi, A., Steinbrunn, W., Pfisterer, M., & Detrano, R. (2025). Heart disease data set. UCI Machine Learning Repository.

Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Khan, M. A., et al. (2021). Machine learning-based diagnosis of heart disease using feature correlation approach. Future Generation Computer Systems.

Chicco, D., & Jurman, G. (2020). The advantages of the matthews correlation coefficient over f1 score and accuracy in binary classification evaluation. BMC Genomics.

Saito, T., & Rehmsmeier, M. (2015). The precision-recall plotis more informative than the roc plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE.

Most read articles by the same author(s)

<< < 2 3 4 5 6 7 8 9 10 11 > >>

International Journal of Modern Computer Science and IT Innovations

A Machine Learning Framework for Predicting Cardiovascular Disease Risk: A Comparative Analysis Using the UCI Heart Disease Dataset

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles