Comparison of a Machine Learning Method and Various Equations for Estimating Low-Density Lipoprotein Cholesterol in Korean Populations

Yu Jin Kwon, Hyangkyu Lee, Su Jung Baik, Hyuk Jae Chang, Ji Won Lee

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3 Citations (Scopus)


Background: LDL-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. Methods: The 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. Results: Our 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. Conclusion: This method may be particularly helpful for managing a patient's cholesterol levels based on their atherosclerotic cardiovascular disease risk.

Original languageEnglish
Article number824574
JournalFrontiers in Cardiovascular Medicine
Publication statusPublished - 2022 Feb 10

Bibliographical note

Funding Information:
This work was supported by the Technology Innovation Program (20002781, A Platform for Prediction and Management of Health Risk Based on Personal Big Data and Lifelogging) funded by the Ministry of Trade, Industry and Energy, Korea, to J-WL; Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through High Value-added Food Technology Development Program funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA; 321030051HD030) to J-WL and Y-JK; the Institute for Information & Communications Technology Promotion grant funded by the Korea government (MSIT; 2019-31-1293, Autonomous digital companion framework and application) to H-JC; and the National Research Foundation of Korea grant funded by the Korea government (MEST; NRF-2019R1A2C1010043) to HL.

Funding Information:
We are grateful for the participants of the KOICA registry and the GSHC cohort. We specially thank the KOICA team (Soyoung Jeon, Donghee Han, Su-Yeon Choi, Eun Ju Chun, Hae-Won Han, Sung Hak Park, Jidong Sung, Hae Ok Jung).

Publisher Copyright:
Copyright © 2022 Kwon, Lee, Baik, Chang and Lee.

All Science Journal Classification (ASJC) codes

  • Cardiology and Cardiovascular Medicine


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