Triglycerides and High-Density Lipoprotein (Hdl): A Nonparametric Regression Approach to Health Assessment

This study develops a regression model that does not rely on specific parameters to evaluate high-density lipoprotein (HDL) levels, with triglycerides (TG) included as an important element. Considering the data's departure from typical patterns, this method provides strength in exploring intricate connections. The approach involves assessing multivariate normality, utilizing nonparametric regression modeling, and dividing the data into training and testing subsets. The Kendall-Theil-Sen-Siegel slope estimator is used to reduce the impact of outliers. Mardia's test indicates notable deviations from normality (skewness ). The evaluation of the model shows impressive predictive performance with RMSE at 20.125 , mean absolute error (MAE) at 17.17, and median absolute error (MedAE) at 17.70. The final model, , offers a dependable framework for grasping TG's influence on HDL levels, enriching clinical insights, and guiding therapeutic choices.