Predictive Modelling Coronary Artery Disease and Hypertension Using Machine Learning

Lavalekar, Maitreya Govind (2024) Predictive Modelling Coronary Artery Disease and Hypertension Using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

A Diseases related to the heart such as Hypertension (HT), and coronary artery disease (CAD) are major global health hazards. The timely prediction can help in performing preventive measures, thus providing a better patient outcome. Present models. Current techniques, despite their progress in predictive modeling, are often inapplicable for generalizing across complex and heterogeneous patient populations. This limitation has reduced their accuracy and reliability in real-world clinical settings, indicating that there is a need for more robust models that could address these issues and provide better prediction performance. In this study, by overcoming the data imbalance issue and utilizing ensemble methods with the data balancing through CTGAN, developed machine learning models that can predict the HT and CAD correctly with high accuracy. HT model generalized well over datasets attaining a test accuracy of 97% with balanced precision and recall. Test accuracy for the CAD model: 92% with a recall of 0.91 of CAD-positive cases, meaning it is able to reliably classify patients at risk of CAD. The CAD model shows minimal overfitting, with the training accuracy at 94%. The findings suggest that balancing the data can improve the accuracy to levels that can be clinically useful, and the ensemble model provides a reliable tool for accurate risk assessment for healthcare providers in the early stages of patient care. Larger datasets and advanced efforts over the model development on how to be more sturdy can be seen in future works.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Qayum, Abdul UNSPECIFIED
Uncontrolled Keywords:	Coronary Artery Disease (CAD); Hypertension (HT); Synthetic data generation; Predictive Modelling; Machine Learning; Ensemble Method
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	03 Sep 2025 11:15
Last Modified:	03 Sep 2025 11:15
URI:	https://norma.ncirl.ie/id/eprint/8734

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