Early Detection of Stunting Based on Feature Engineering Approach

Abstract

The stunting problem in Indonesia is still an extensive issue for the government. Around 22% of cases of stunting affect brain development, resulting in reduced intellectual capacity and permanent disruption of the structure and function of nerves and brain cells. This research describes early detection of stunting using an  feature selection approach. So, datasets related to stunting are valuable in providing complete insight or information in detecting early symptoms of stunting in toddlers. Machine learning modelling for early detection of stunting in this study shows that of the 14 features predicting the value of detecting stunting, only seven features are influential based on their correlation values. When testing continues using Machine Learning algorithms with various variants, the Multilayer Perceptron algorithm can produce an accuracy value of 98%.