Application of min-max normalization in backpropagation to detect early malnutrition

Abstract

Malnutrition is the nutritional condition of toddlers which is characterized by a very thin condition, along with or without edema on the backs of both legs, weight according to body length or weight compared to height less than -3 standard deviations and/or upper arm circumference less than 11.5 cm in children aged 0-59 months. The contribution of this study is to apply min-max normalization to detect malnutrition using the Backpropagation method. This study uses the Backpropagation method with CRISP-DM, the input value of the artificial neural network is in an infinite range to a finite output value, which is in a range of 0 to 1. In order for this value to be met, a min-max normalization is carried out on the input value. The test results on this prototype achieved a maximum accuracy value of k= 1 with an accuracy value of 69%, Precision of 94% and Sensitivity of 69%. The results of the recommendation of the condition of children who are detected with malnutrition are very useful as an initial preventive measure in order to improve the nutritional condition of children with the right special treatment early and comprehensively.