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Pharmacokinetic Models: Comparison and Selection Criterion

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Related Experiment Video

Updated: Jul 16, 2026

LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring
08:45

LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring

Published on: November 17, 2018

Comparative Evaluation of Machine Learning Models and Conventional Formulas for LDL Cholesterol Estimation.

Bagnu Orhan1, Levent Deniz1, Cengiz Aydin2

  • 1Department of Medical Biochemistry, University of Health Sciences, Istanbul Training and Research Hospital, 34098 Istanbul, Türkiye.

Diagnostics (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

Machine learning models offer a more accurate way to estimate low-density lipoprotein cholesterol (LDL-C), especially for individuals with high triglyceride levels. These advanced models outperform traditional formulas in clinical settings.

Keywords:
Friedewald formulaMartin formulaSampson formulahypertriglyceridemialow-density lipoprotein cholesterol (LDL-C)machine learning

Related Experiment Videos

Last Updated: Jul 16, 2026

LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring
08:45

LDL Cholesterol Uptake Assay Using Live Cell Imaging Analysis with Cell Health Monitoring

Published on: November 17, 2018

Area of Science:

  • Biomedical Informatics
  • Clinical Chemistry
  • Machine Learning in Healthcare

Background:

  • Accurate estimation of low-density lipoprotein cholesterol (LDL-C) is crucial for cardiovascular risk assessment.
  • Conventional formulas for LDL-C estimation exhibit limitations, particularly in hypertriglyceridemic individuals.
  • Machine learning (ML) presents a potential avenue for improving LDL-C estimation accuracy.

Purpose of the Study:

  • To develop and validate ML models for LDL-C estimation.
  • To compare the analytical and clinical performance of ML models against conventional formulas.
  • To evaluate model performance in individuals with elevated triglyceride (TG) levels.

Main Methods:

  • Retrospective analysis of lipid profiles from 11,681 adults.
  • Development and evaluation of multiple ML models (linear regression, random forest, support vector regression, XGBoost) using 10-fold cross-validation.
  • Performance assessment via error metrics (MAE, RMSE), bias, correlation, Bland-Altman analysis, and clinical classification accuracy, with subgroup analysis for TG levels (TG ≥ 400 mg/dL).

Main Results:

  • ML models, particularly XGBoost, demonstrated superior performance with lower error and higher agreement compared to conventional formulas.
  • XGBoost achieved MAE of 14.7 mg/dL, RMSE of 20.22 mg/dL, and R² of 0.780.
  • ML models maintained stability and higher clinical classification accuracy (up to 66%) across increasing TG levels, outperforming conventional methods in hypertriglyceridemic populations.
  • External validation confirmed the stable performance and superior classification accuracy of the XGBoost model.

Conclusions:

  • ML-based LDL-C estimation provides a robust alternative to conventional formulas.
  • These ML models are particularly beneficial for improving LDL-C estimation accuracy in patients with hypertriglyceridemia.
  • The findings support the integration of ML models into clinical practice for more precise lipid management.