The global population of malnourished children has been declining for the past 30 years; however their are growing concerns that underdeveloped countries are being left behind. This research applies machine learning algorithms to a WHO and World Bank child malnutrition data set to predict the four types of malnutrition: stunting, wasting, underweight and overweight. The goal of this report is to compare previous studies to the machine learning algorithms in hopes to find new conclusions. In addition, the report investigates the merits and demerits of machine learning algorithms like classification and regression trees (CART), bagged CART and random forest. The comparative analysis provides insight on how decision trees are more advantageous for prediction compared to traditional methods like multiple linear regression. The family of recursively partitioned decision trees all behave similarly; however this research discovers the nuances between them. The classification and regression tree extension algorithms provide greater accuracy in prediction results, but lack the ability for interpretations. The machine learning algorithms constructed in this research supply quantifiable interpretations and visualizations that drive discussion for limiting the global population of malnourished children.
Sansom, Kory, "Gardening Algorithms for the Fruitful Future: Decision Trees and Random Forest Algorithms to Predict Child Malnutrition Populations Globally" (2019). Senior Independent Study Theses. Paper 8366.
child malnutrition, machine learning, classification and regression trees, random forest, bootstrap aggregation, bagging, stunting, wasting, underweight, overweight
Bachelor of Arts
Senior Independent Study Thesis
© Copyright 2019 Kory Sansom