Deep learning-based LDL-C level prediction and explainable AI interpretation

Abstract

This study investigates the use of deep learning (DL) models to predict low-density lipoprotein cholesterol (LDL-C) levels. The dataset obtained from New York-Presbyterian Hospital/Weill Cornell Medical Center includes triglycerides (TG), total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C). LDL-C prediction was performed using DL models such as CNN, RNN and LSTM and the results were compared with traditional machine learning (ML) and LDL-C formulas. The obtained results showed that DL models are more successful than traditional formulas while giving closer results to ML models. It is shown that DL models can predict LDL-C with higher accuracy compared to the Sampson, and Martin equation. In particular, RNN and LSTM models performed better in LDL-C prediction than the other formulas. In addition, the prediction results of DL models were explained using Local Interpretable Model-Agnostic Explanations (LIME) method. The features of the proposed models provide more parameters to explain the AI Model better in comparison with the ML models but require more computational efforts to explain DL model decisions. The results demonstrate that DL models in predicting LDL-C levels are more effective than traditional methods for LDL-C prediction and can be used in clinical applications. As a result, the findings might provide significant contributions to assessing cardiovascular disease risk and planning treatment protocols.