ArticleBMC medical research methodology2018
Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk.
Article in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 4 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational Challenges.Vascular health and risk management · 2026Pooled it
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis.European heart journal. Quality of care & clinical outcomes · 2023Pooled it
- Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review.Heart failure reviews · 2021Pooled it
- Identification of key cardiovascular disease predictive factors from the China Health and Retirement Longitudinal Study dataset using machine learning-based algorithms.Journal of translational medicine · 2026Article
- Geospatial and machine learning analyses of cardiovascular disease mortality across the continental United States: Identifying associated variables using Shapley values.BMC public health · 2026Article
- Optimization of ultrasonic extraction and kinetic modeling of jieduquyuziyin prescription polysaccharides via explainable machine learning.Ultrasonics sonochemistry · 2025Article
- Machine learning in predicting preoperative intra-aortic balloon pump use in patients undergoing coronary artery bypass grafting.Journal of cardiothoracic surgery · 2025Article
- Predicting incident cardio-metabolic disease among persons with and without depressive and anxiety disorders: a machine learning approach.Social psychiatry and psychiatric epidemiology · 2025Article
- Cardiovascular Risk Estimation in Colombia Using Artificial Intelligence Techniques.Cardiology research and practice · 2025Article
- Detecting cardiovascular diseases using unsupervised machine learning clustering based on electronic medical records.BMC medical research methodology · 2024Article
- The value of CCTA combined with machine learning for predicting angina pectoris in the anomalous origin of the right coronary artery.Biomedical engineering online · 2024Article
- Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.Journal of medical Internet research · 2024Article
- Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data.BMC public health · 2024Article
- Random kernel k-nearest neighbors regression.Frontiers in big data · 2024Article
- Comparison of LASSO and random forest models for predicting the risk of premature coronary artery disease.BMC medical informatics and decision making · 2023Article
- Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches.Current atherosclerosis reports · 2023Review
- Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques.BMC medical informatics and decision making · 2023Review
- Comparison of State-of-the-Art Neural Network Survival Models with the Pooled Cohort Equations for Cardiovascular Disease Risk Prediction.BMC medical research methodology · 2023Article
- Machine learning-based marker for coronary artery disease: derivation and validation in two longitudinal cohorts.Lancet (London, England) · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundThe use of Cardiovascular Disease (CVD) risk estimation scores in primary prevention has long been established. However, their performance still remains a matter of concern. The aim of this study was to explore the potential of using ML methodologies on CVD prediction, especially compared to established risk tool, the HellenicSCORE.
methodsData from the ATTICA prospective study (n = 2020 adults), enrolled during 2001-02 and followed-up in 2011-12 were used. Three different machine-learning classifiers (k-NN, random forest, and decision tree) were trained and evaluated against 10-year CVD incidence, in comparison with the HellenicSCORE tool (a calibration of the ESC SCORE). Training datasets, consisting from 16 variables to only 5 variables, were chosen, with or without bootstrapping, in an attempt to achieve the best overall performance for the machine learning classifiers.
resultsDepending on the classifier and the training dataset the outcome varied in efficiency but was comparable between the two methodological approaches. In particular, the HellenicSCORE showed accuracy 85%, specificity 20%, sensitivity 97%, positive predictive value 87%, and negative predictive value 58%, whereas for the machine learning methodologies, accuracy ranged from 65 to 84%, specificity from 46 to 56%, sensitivity from 67 to 89%, positive predictive value from 89 to 91%, and negative predictive value from 24 to 45%; random forest gave the best results, while the k-NN gave the poorest results.
conclusionsThe alternative approach of machine learning classification produced results comparable to that of risk prediction scores and, thus, it can be used as a method of CVD prediction, taking into consideration the advantages that machine learning methodologies may offer.
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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.