Comparison of conventional formulas and machine learning models for estimating serum low-density lipoprotein...

Accurate estimation of low-density lipoprotein cholesterol (LDL-C) is essential for cardiovascular risk assessment and monitoring. Because β-quantification is labor-intensive and impractical for routine use, several equations have been developed to estimate LDL-C indirectly, including the widely used Friedewald formula. This study evaluated established LDL-C equations and multiple machine learning models against directly measured LDL-C across different triglyceride and LDL cholesterol levels.

A total of 303 serum samples, categorized by triglyceride levels, were analyzed for LDL-C using a direct homogeneous assay on the Roche Cobas® e6000 platform and compared with values estimated by twelve established equations. Agreement was assessed using correlation analysis, mean bias, and Bland-Altman plots. In addition, supervised machine learning models were developed using an 60:40 training-testing split to evaluate predictive performance.

All equations except Ahmadi showed strong correlation with direct LDL-C (r > 0.90). The Puavilai and DeLong formulas demonstrated the highest correlation (r ≈ 0.97), with Puavilai showing the lowest mean bias (0.005 mM). Among machine learning models, Artificial Neural Network (ANN), linear regression, and ridge regression achieved the highest correlation (R = 0.97). The ANN showed minimal bias (0.04 mM), while most other models slightly underestimated LDL-C. The Friedewald formula demonstrated increasing positive bias at higher triglyceride levels.

The Puavilai formula and ANN model showed superior agreement with direct LDL-C measurements compared with the Friedewald equation. These findings suggest that alternative equations, particularly Puavilai, may improve LDL-C estimation in routine clinical practice in Oman. Further multicenter validation is warranted before national implementation.