Abstract :
Despite being one of the fastest-growing economies, the Philippines still experiences pressing health issues such as malnutrition, which is characterized as either overweight or underweight and generally defined as a nutritional deficiency. As a result, various government agencies, in collaboration with Local Government Units (LGUs) and other partner Non-Governmental Organizations (NGOs), are working to address these nutrition-related issues. In Cabanatuan City, the City Nutrition Committee (CNC) prepared the City Nutrition Action Plan (CNAP) to improve the nutritional status of those tested and address the core causes of malnutrition. With this setting in mind, the goal of this study was to use a data mining approach to do a predictive analysis of malnutrition among children aged six (6) years and under in Cabanatuan City. The study was carried out at 19 Day Care Centers (DCC) in District IV of Cabanatuan City. The population of children designated as malnourished – either underweight or overweight – from 2016 to 2018 was used in the prediction analysis. In this work, data mining and clustering techniques were applied. The analysis showed that in the three (3) years following the period under survey, underweight cases would be decreasing while overweight cases would be increasing. This study concludes that data mining and clustering methods, with the help of predictive analysis assessment, are appropriate in predicting certain phenomena, such as malnutrition cases. However, it is recommended that further studies be conducted to determine and explore more variables that bear on the malnutrition among children in the City of Cabanatuan. Moreover, an in-depth predictive analysis of malnutrition in the area may also be conducted. The analysis results help develop policies and regulations that will help overcome the malnutrition problem in the country.
Keyword :
Cabanatuan city, Data mining, K-means, Clustering, Predictive analysis.