ArticleFrontiers in cardiovascular medicine2022
Comparison of a Machine Learning Method and Various Equations for Estimating Low-Density Lipoprotein Cholesterol in Korean Populations.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 16 citations in OpenAlex.
- Comparative evaluation of LDL-cholesterol estimation using machine learning algorithms and conventional equations in a large middle eastern cohort.Clinics (Sao Paulo, Brazil) · 2026Article
- Neural network model outperforms conventional equations in LDL cholesterol estimation: A comparative study of 188,887 Chinese individuals with focus on hypertriglyceridemia.Atherosclerosis plus · 2025Article
- Nonfasting, Telehealth-Ready LDL-C Testing With Machine Learning to Improve Cardiovascular Access and Equity.medRxiv : the preprint server for health sciences · 2025Article
- Integrating New Technologies in Lipidology: A Comprehensive Review.Journal of clinical medicine · 2025Review
- Applicability of Artificial Intelligence in the Field of Clinical Lipidology: A Narrative Review.Journal of lipid and atherosclerosis · 2024Review
- Comparison of Newly Proposed LDL-Cholesterol Estimation Equations.Journal of Korean medical science · 2023Article
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Authors and funding
5 authors at 2 institutions in 1 country.
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Abstract
backgroundLDL-C is the primary target of lipid-lowering therapy and used to classify patients by cardiovascular disease risk. We aimed to develop a deep neural network (DNN) model to estimate LDL-C levels and compare its performance with that of previous LDL-C estimation equations using two large independent datasets of Korean populations.
methodsThe final analysis included participants from two independent population-based cohorts: 129,930 from the Gangnam Severance Health Check-up (GSHC) and 46,470 participants from the Korean Initiatives on Coronary Artery Calcification registry (KOICA). The DNN model was derived from the GSHC dataset and validated in the KOICA dataset. We measured our proposed model's performance according to bias, root mean-square error (RMSE), proportion (P)10-P20, and concordance. P was defined as the percentage of patients whose LDL was within ±10-20% of the measured LDL. We further determined the RMSE scores of each LDL equation according to Pooled cohort equation intervals.
resultsOur DNN method has lower bias and root mean-square error than Friedewald's, Martin's, and NIH equations, showing a high agreement with LDL-C measured by homogenous assay. The DNN method offers more precise LDL estimation in all pooled cohort equation strata.
conclusionThis method may be particularly helpful for managing a patient's cholesterol levels based on their atherosclerotic cardiovascular disease risk.
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