ArticleDiagnostics (Basel, Switzerland)2021
Machine Learning-Based Cardiovascular Disease Prediction Model: A Cohort Study on the Korean National Health Insurance Service Health Screening Database.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
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- Supervised Machine Learning Algorithms for Fitness-Based Cardiometabolic Risk Classification in Adolescents.Sports (Basel, Switzerland) · 2025Article
- Atherosclerotic Cardiovascular Disease Risk Prediction Models in China, Japan, and Korea: Implications for East Asians?JACC. Asia · 2025Review
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- Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.European heart journal. Digital health · 2025Review
- Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases.Diagnostics (Basel, Switzerland) · 2024Article
- Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey.BMJ health & care informatics · 2023Article
- Survival Prediction Model for Patients with Hepatocellular Carcinoma and Extrahepatic Metastasis Based on XGBoost Algorithm.Journal of hepatocellular carcinoma · 2023Article
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- Explainable machine learning framework for predicting long-term cardiovascular disease risk among adolescents.Scientific reports · 2022Article
- Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.Healthcare (Basel, Switzerland) · 2022Article
- Validation of risk prediction models applied to longitudinal electronic health record data for the prediction of major cardiovascular events in the presence of data shifts.European heart journal. Digital health · 2022Article
- Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients.Scientific reports · 2022Article
- Machine learning-based diagnosis and risk factor analysis of cardiocerebrovascular disease based on KNHANES.Scientific reports · 2022Article
- Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea.Diagnostics (Basel, Switzerland) · 2021Article
- Review
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6 authors.
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Abstract
backgroundThis study proposes a cardiovascular diseases (CVD) prediction model using machine learning (ML) algorithms based on the National Health Insurance Service-Health Screening datasets.
methodsWe extracted 4699 patients aged over 45 as the CVD group, diagnosed according to the international classification of diseases system (I20-I25). In addition, 4699 random subjects without CVD diagnosis were enrolled as a non-CVD group. Both groups were matched by age and gender. Various ML algorithms were applied to perform CVD prediction; then, the performances of all the prediction models were compared.
resultsThe extreme gradient boosting, gradient boosting, and random forest algorithms exhibited the best average prediction accuracy (area under receiver operating characteristic curve (AUROC): 0.812, 0.812, and 0.811, respectively) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the CVD prediction performance, compared to previously proposed prediction models. Preexisting CVD history was the most important factor contributing to the accuracy of the prediction model, followed by total cholesterol, low-density lipoprotein cholesterol, waist-height ratio, and body mass index.
conclusionsOur results indicate that the proposed health screening dataset-based CVD prediction model using ML algorithms is readily applicable, produces validated results and outperforms the previous CVD prediction models.
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