Abstract 12546: Artificial Neural Network Models for Lipid Profile Prediction in the US General Population

Abstract

Introduction: Hyperlipidemia is a major risk factor for cardiovascular disease. Tests of blood lipid concentrations require a blood sample to be taken in a clinical setting, which is costly, time-consuming, and may preclude measurements in a large number of people at risk. Hypothesis/Objective: This study aimed to develop artificial neural network (ANN) models to predict lipid profiles of the US general population using noninvasive and low-cost diagnostic features. Methods: We used the most recent cycle of the National Health and Nutrition Examination Survey (NHANES) 2017-2018. The participants with missing values in outcomes: total cholesterol (TCH), triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL), and aged<18 years were excluded. We developed ANN-based prediction models using various combinations of eight features: age, gender, race/ethnicity, body mass index, waist to hip ratio, waist to height ratio, systolic and diastolic blood pressure with three to thirty hidden neurons in one layered feed-forward ANN. We trained ANN models 100 times for seven different input combinations. The performances of these prediction models were evaluated by mean accuracy (ACC), R-squared (R 2 ), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). Results: Of 5176 participants, 51.8% were females with a mean (range) age of 52 (18-80) years. Results show that the ANN models can predict these outcomes with high mean ACC, (TCH 96.69%, HDL 95.11%, LDL 93.47%, TG 91.35%), average R2 (TCH 0.022, HDL 0.186, LDL -0.019, TG 0.158), average MSE (TCH 0.048, HDL 0.056, LDL 0.152, TG 0.268), average RMSE (TCH 0.220, HDL 0.237, LDL 0.389, TG 0.518), and average MAE (TCH 0.171, HDL 0.192, LDL 0.287, TG 0.410). Conclusions: ANN is an effective diagnostic tool for predicting lipid profiles for the US general population, and can be used to predict lipid profiles for clinical diagnosis using noninvasive and low-cost features.