ArticleScientific reports2022
Machine learning-based diagnosis and risk factor analysis of cardiocerebrovascular disease based on KNHANES.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
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- Genetic homocysteine risk shapes cardiocerebral structure and multimorbidity through age- and sex-specific mechanisms: a UK Biobank study.Frontiers in nutrition · 2025Article
- Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer.Scientific reports · 2024Article
- Interpretability-based machine learning for predicting the risk of death from pulmonary inflammation in Chinese intensive care unit patients.Frontiers in medicine · 2024Article
- Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables.Nutrients · 2023Article
- Exploring sex disparities in cardiovascular disease risk factors using principal component analysis and latent class analysis techniques.BMC medical informatics and decision making · 2023Review
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- Machine learning-based risk factor analysis of necrotizing enterocolitis in very low birth weight infants.Scientific reports · 2022Article
- Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach.Cardiovascular diabetology · 2022Observational
- Puerarin attenuates isoproterenol‑induced myocardial hypertrophy via inhibition of the Wnt/β‑catenin signaling pathway.Molecular medicine reports · 2022Article
- Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999)Article
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9 authors.
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Abstract
The prevalence of cardiocerebrovascular disease (CVD) is continuously increasing, and it is the leading cause of human death. Since it is difficult for physicians to screen thousands of people, high-accuracy and interpretable methods need to be presented. We developed four machine learning-based CVD classifiers (i.e., multi-layer perceptron, support vector machine, random forest, and light gradient boosting) based on the Korea National Health and Nutrition Examination Survey. We resampled and rebalanced KNHANES data using complex sampling weights such that the rebalanced dataset mimics a uniformly sampled dataset from overall population. For clear risk factor analysis, we removed multicollinearity and CVD-irrelevant variables using VIF-based filtering and the Boruta algorithm. We applied synthetic minority oversampling technique and random undersampling before ML training. We demonstrated that the proposed classifiers achieved excellent performance with AUCs over 0.853. Using Shapley value-based risk factor analysis, we identified that the most significant risk factors of CVD were age, sex, and the prevalence of hypertension. Additionally, we identified that age, hypertension, and BMI were positively correlated with CVD prevalence, while sex (female), alcohol consumption and, monthly income were negative. The results showed that the feature selection and the class balancing technique effectively improve the interpretability of models.
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