Ability of ordinal spline logistic regression model in the classification of nutritional status data

Abstract

: In this study, an ordinal spline logistic regression model was developed and used to classify data on the nutritional status of children under five in the Gowa district, Indonesia. The nutritional status of toddlers consists of 3 categories: malnutrition, good nutrition, and excess nutrition. So nutritional status data for toddlers can be modeled by ordinal spline logistic regression. The results of this study indicate that the data on the nutritional status of children is optimal in the ordinal spline logistic regression model using 2-knot points with a GCV value of 0.2158. The estimation results of the ordinal spline logistic regression model show that toddlers aged 18 months and 24 months tend to have a good chance of getting good nutrition. In comparison, toddlers aged 18 to 24 months tend to have a minimal chance of getting good nutrition, and the accuracy of the classification model of the nutritional status of toddlers uses the ordinal spline logistic regression of 92.25%.