PE-M: Prediction of malnutrition of Barangay 35 Tondo Manila using random forest algorithm

Abstract

Malnutrition is still an ongoing problem in the Philippines as it poses a threat especially to children. There are multiple considerations for malnutrition in order to help find the root cause of malnutrition for each child, it is quite a tedious task to perform. According to the classification of undernutrition rates, the prevalence in the Philippines is of "extremely high" public health relevance. It was stated that in 2019, the prevalence of underweight and wasting was 19% and 6%, respectively. Poverty amplifies the risk of, and risks from, malnutrition. People who are poor are more likely to be affected by different forms of malnutrition. Also, malnutrition increases health care costs, reduces productivity, and slows economic growth, which can perpetuate a cycle of poverty and ill-health. The application Pe-M, is a web application which is aimed to predict the malnutrition rate within Barangay 35 in Tondo Manila City and also give food recommendations based on the classification the children are categorized. The application uses Random Forest Algorithm in calculating the prediction from the data provided in order to calculate the percentage of malnutrition present in Barangay 35 Tondo Manila City. To ensure that the web application performs as per its specified requirements, a comprehensive specification and evaluation of software product quality was conducted using the ISO 25010 software quality model. This study specifically seeks to evaluate the web application in terms of performance efficiency and usability. The following results show that there are kids that fall in the classification of malnutrition showing that there is malnutrition in the barangay. The confusion matrix shows that RFA gives 87.80% accuracy based on the risk factors given and the trained data in the system. The RFA predicted that 21.95% of the data is said to be the malnutrition rate in the barangay based on the given data and the highest risk factor in the data with 3.6% is mineral water consumption of the citizens. The model identified the risk factors which helped predict malnutrition rate making the model more reliable and transparent. CCS CONCEPTS • Random Forest Algorithm • Classification • Malnutrition• Percentage Calculation