ArticleOpen heart2025
Comparative study of coronary artery disease prediction: conventional QRISK3 versus enhanced machine learning models combined with particle swarm optimisation algorithm.
Article in Open heart, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCoronary 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.
methodsThis 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.
resultsOut 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.
conclusionsThis 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.