Predicting dyslipidemia in Chinese elderly adults using dietary behaviours and machine learning algorithms

Objectives: We aimed to predict dyslipidemia risk in elderly Chinese adults using machine learning and dietary analysis for public health. Study design: This cross-sectional study includes 13,668 Chinese adults aged 65 or older from the 2018 Chinese Longitudinal Healthy Longevity Survey. Methods: Dyslipidemia prediction was carried out using a variety of machine learning algorithms, including Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Gaussian Naive Bayes (GNB), Gradient Boosting Machine (GBM), Adaptive Boosting Classifier (AdaBoost), Light Gradient Boosting Machine (LGBM), and K-Nearest Neighbour (KNN), as well as conventional logistic regression (LR). Results: The prevalence of dyslipidemia among eligible participants was 5.4 %. LGBM performed best in predicting dyslipidemia, followed by LR, XGBoost, SVM, GBM, AdaBoost, RF, GNB, and KNN (all AUC > 0.70). Frequency of nut product consumption, childhood water source, and housing types were key predictors for dyslipidemia. Conclusions: Machine learning algorithms that integrated dietary behaviours accurately predicted dyslipidemia in elderly Chinese adults. Our research identified novel predictors such as the frequency of nut product consumption, the main source of drinking water during childhood, and housing types, which could potentially prevent and control dyslipidemia in elderly adults.