Machine Learning-Based Early Cardiovascular Disease Prediction: A Comparative Analysis of Supervised Learning Algorithms Using a Pakistani Clinical Dataset

Abstract

Cardiovascular Diseases (CVDs) continue to be one of the leading causes of deaths in the world, claiming some 17.9 million lives every year. This burden is higher in Pakistan because of "Asian Indian Phenotype" which makes them vulnerable to early coronary artery disease. The commonly used traditional risk prediction models, including the Framingham Risk Score, have been developed in Western populations and are poorly predictive in South Asian populations. This study aims to fill this important gap by designing, implementing and comparative evaluation of six supervised machine learning algorithms for early detection of cardiovascular disease using a locally collected clinical dataset of 411 patient records with 13 independent clinical attributes. The models tested are Logistic Regression, K Nearest Neighbor, Support Vector Machine, Random Forest, Gradient Boosting and XGBoost. A rigorous gender-based mean imputation and Z-score normalization was done and split in 80/20 ratio. Empirical results show that the Random Forest classifier has Area under the Curve (AUC) of 0.9842, accuracy of 95.2%, precision of 96.0% and recall of 96.0%. The model was then exported and used to create a browser-based, predictive application that could be embedded in an interactive dashboard for real-time cardiovascular risk without the need for a server. These results confirm the effectiveness of ensemble learning approaches for medical diagnostics and highlight the potential of implementing ML-based screening tools in the limited resource healthcare environment in Pakistan.