A machine learning-based approach for low-density lipoprotein cholesterol calculation using age, and lipid parameters

Low-density lipoprotein cholesterol (LDL-C) is a critical biomarker for cardiovascular disease. However, no consensus exists on the best method for estimating LDL-C in Chinese laboratories. This study aimed to develop a machine learning (ML) method for LDL-C estimation.

An extensive data set of 111,448 samples were randomized into five equal subsets. ML-based equations were developed using age, sex, and lipid parameters based on five-fold cross-validation. The trained ML equations were externally validated in three different data sets. The performance of the ML equations was compared with the Friedewald, Martin/Hopkins, and Sampson equations.

The selected ML equations showed less bias with direct LDL-C than other LDL-C equations in the Chinese population, including those with triglycerides (TG) ≥ 400 mg / dL and LDL-C < 40 mg / dL. The performance of the ML equations was less susceptible to age. External validation showed the generalization of the ML equations.

This study highlights the potential of integrating sex, age, and lipid parameters into the ML equations to obtain a more robust and reliable LDL-C calculation.