Comparative study of coronary artery disease prediction - conventional QRISK3 versus enhanced machine learning models combined with Particle Swarm Optimization algorithm

Aug 3, 2026

Coronary artery disease (CAD) is a major cause of global mortality, making early risk stratification essential for effective primary prevention. Conventional QRISK3 may overestimate future CAD risk in some populations, leading to unnecessary preventive treatment that can reduce cost-effectiveness and safety. This study evaluates whether hybrid machine learning models optimized with Particle Swarm Optimization can improve CAD prediction by selecting high-performing feature subsets tied to clinical outcomes.

LanguageEnglish

CategoryResearch & Report

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Pages9

Reading Time23 min

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Open access Coronary artery disease Comparative study of coronary artery disease prediction: conventional QRISK3 versus enhanced machine learning models combined with particleswarm optimisation algorithm Wigaviola Socha Purnamaasri Harmadha ,1,2 Dennis Wang,1,3,4 Mohsin Masood1,5 To cite: Harmadha WSP, Wang D, Masood M. Comparative study of coronary artery disease prediction: conventional QRISK3 versus enhanced machine learning models combined with particleswarm optimisation algorithm. Open Heart 2025;12:e003422 . doi:10.1136/ openhrt-2025-003422 DW and MM are joint senior authors. Received 30 April 2025 Accepted 2 October 2025 © Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY. Published by BMJ Group. 1National Heart and Lung Institute, Imperial College London, London, UK 2Faculty of Medicine, Airlangga University, Surabaya, Indonesia 3Institute for Human Development and Potential, Agency for Science, Technology and Research (A*STAR), Singapore 4Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore 5Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK Correspondence to Dr Dennis Wang; [dennis.wang@](dennis.wang@)[ ](dennis.wang@)[imperial.ac.uk](imperial.ac.uk) ABSTRACT Background Coronary artery disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known to overestimate future CAD risk in some populations, resulting in unnecessary preventive treatment that reduces the cost-effectiveness and safety. Combining machine learning with a metaheuristic optimisation approach using the Particle Swarm Optimization algorithm may outperform QRISK3 in predicting CAD. It may improve performance by selecting the best-performing subset of features related to clinical outcomes. Methods This study uses the UK Biobank dataset consisting of 348 015 participants aged 24–84 years with no prior diagnosis of CAD. The performance of both QRISK3 and machine learning models was evaluated separately using receiver operating characteristic analysis. Several machine learning models were assessed: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes and Gradient Boosting. The dataset was split into training and test sets with a ratio of 4:1 for the machine learning models. Each model has been developed by adding a Particle Swarm Optimization algorithm to enhance the model’s classification accuracy. Results Out of 348 015 participants, 23 136 individuals (6 .64%) were diagnosed with CAD within 10 years following their first visit, while 324 879 individuals (93.4%) did not develop CAD. The area under the curve (AUC) value of the QRISK3 prediction was 0.6113, while the gradient boosting model using Particle Swarm Optimization achieved a better performance AUC of 0.7258. Conclusions This study shows hybrid machine learning models optimised with the Particle Swarm Optimization algorithm can better predict CAD than QRISK3 . The application of such machine learning models can effectively identify high-risk CAD patients, allowing for more personalised preventative strategies and supporting policymakers in implementing lifestyle change recommendations. WHAT IS ALREADY KNOWN ON THIS TOPIC ⇒ Conventional risk assessment tools such as Framingham Risk Score, pooled cohort equation, Systematic Coronary Risk Evaluation and QRISK3 have been widely used to predict an individual’s risk of developing coronary artery disease (CAD) and apply primary prevention. ⇒ In previous studies, QRISK3 has been shown to overestimate the risk of CAD in most populations, reducing the cost-effectiveness of preventive treatment. ⇒ Machine learning models potentially outperform QRISK3 in forecasting an individual’s risk of having CAD in the next decade. INTRODUCTION Coronary artery disease (CAD) is a prevalent cardiovascular disease

FAQs

Why is CAD risk stratification important in primary prevention?

Early detection depends on accurate risk stratification to target prevention efforts. It supports timely interventions and helps improve outcomes.

How does this study compare QRISK3 with machine learning approaches?

The study evaluates QRISK3 and multiple machine learning models using receiver operating characteristic analysis, then enhances each model with Particle Swarm Optimization to improve classification accuracy.

Which model showed the best predictive performance and what was the result?

The gradient boosting model combined with Particle Swarm Optimization achieved a higher AUC (0.7258) than QRISK3 (0.6113), indicating improved prediction of CAD within a decade.

Aug 3, 2026

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