Bi-response truncated spline nonparametric regression with optimal knot point selection using generalized cross-validation in diabetes mellitus patient's blood sugar levels

Abstract

This article discusses statistical modeling implemented in the health sector. This study used a bi-response nonparametric regression method with truncated spline estimation that used two response variables. The nonparametric regression method is used when the regression curve is not known for its shape and pattern. This study aims to model the blood sugar levels of people with diabetes mellitus. The data used are blood sugar levels of people with diabetes mellitus before fasting, blood sugar levels of people with diabetes mellitus two hours after fasting, cholesterol levels, and triglyceride levels. Determination of the optimal knot point using Generalized Cross-Validation. The parameter estimation method used is Weighted Least-Squares. The best model was obtained from the study results, namely the bi-response truncated spline regression model with three-knot points where the minimum GCV value is 8.573 and has an R² value of 99.62%.