Estimation of Low-Density Lipoprotein Cholesterol Concentration Using Machine Learning


Çubukçu H. C., Topcu D. İ.

Lab Medicine, vol.53, no.2, pp.161-171, 2022 (SCI-Expanded, Scopus)

  • Publication Type: Article / Article
  • Volume: 53 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.1093/labmed/lmab065
  • Journal Name: Lab Medicine
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, CINAHL, EMBASE, MEDLINE
  • Page Numbers: pp.161-171
  • Keywords: low-density lipoproteins, cholesterol, lipids, artificial intelligence, machine learning, lipoproteins, LDL-CHOLESTEROL, CLINICAL-CHEMISTRY, EQUATION
  • Ankara University Affiliated: Yes

Abstract

© American Society for Clinical Pathology, 2021. All rights reserved.Objective: Low-density lipoprotein cholesterol (LDL-C) can be estimated using the Friedewald and Martin-Hopkins formulas. We developed LDL-C prediction models using multiple machine learning methods and investigated the validity of the new models along with the former formulas. Methods: Laboratory data (n = 59,415) on measured LDL-C, high-density lipoprotein cholesterol, triglycerides (TG), and total cholesterol were partitioned into training and test data sets. Linear regression, gradient-boosted trees, and artificial neural network (ANN) models were formed based on the training data. Paired-group comparisons were performed using a t-test and the Wilcoxon signed-rank test. We considered P values <.001 with an effect size >.2 to be statistically significant. Results: For TG ≥177 mg/dL, the Friedewald formula underestimated and the Martin-Hopkins formula overestimated the LDL-C (P <.001), which was more significant for LDL-C <70 mg/dL. The linear regression, gradient-boosted trees, and ANN models outperformed the aforementioned formulas for TG ≥177 mg/dL and LDL-C <70 mg/ dL based on a comparison with a homogeneous assay (P >.001 vs. P <.001) and classification accuracy. Conclusion: Linear regression, gradient-boosted trees, and ANN models offer more accurate alternatives to the aforementioned formulas, especially for TG 177 to 399 mg/dL and LDL-C <70 mg/dL.