Predicting Kids Malnutrition Using Multilayer Perceptron with Stochastic Gradient Descent

Received: 6 June 2020 Accepted: 10 October 2020 The capability of predicting malnutrition kids is highly beneficial to take remedial actions on kids who are under 5 year’s age. In this article, Kid’s malnutrition predictive model is created and tested with our own collected dataset. We find the issues of kids malnutrition by the use of Machine Learning (ML) models. From ML-models, a multi-layer perceptron is used to classify the data neatly. Optimizing technique stochastic gradient descent (SGD) and Multilayer Perceptron (MLP) classifier methods are integrated to classify the data more effectively. To select the best features, from the feature selection (FS) technique filter-based method used. After selecting the best features, selected features are pass to the classifier model then the model will classify the data. Results with the MLP-SGD classifier were good than the other classifiers but after feature selection, the performance of the model was increased more. It will help in improving the analysis of malnutrition kid’s data. The sample data are collected from parents who are having kids less than five years of age at Repalle town, Andhra Pradesh, India.