Malnutrition Prediction Using Decision Tree and Random Forest Algorithm

Abstract

Abstract— Malnutrition remains a significant public health concern globally, particularly affecting vulnerable populations such as infants and children. Malnutrition refers to the imbalance between the intake of nutrients and the body's requirements, leading to adverse health outcomes. Ensemble learning is a machine learning approach that combines the predictions of multiple base models to improve predictive performance. This study employs two popular ensemble techniques: Random Forest and Decision Trees. Random Forest constructs a multitude of decision trees and aggregates their predictions, while Decision Trees partition the feature space to predict the target variable. The dataset used in this study comprises features related to infant health, demographics, feeding patterns, and growth metrics. By leveraging Random Forest and Decision Trees, this research aims to identify key factors contributing to infant malnutrition and develop accurate predictive models.