Analyzing the Malnutrition Valuation on Legazpi City using Data Analytics

Abstract

A Data analytics predictive analysis aids to unlock the knowledge of the decision maker in the development of the organization to addressing the malnutrition in implementing government projects in the City of Legazpi, Philippines. Malnutrition is one of the results of poverty in the country mostly the younger age Filipinos. The study aims to apply Data Analytics in analyzing the factor that affects its malnutrition. The researchers evaluated the parameters that have significant contribution in deciding malnutrition. The correlation of the parameters in deciding malnutrition and the level of malnutrition per barangay in the city were also determined. The Rural Health Unit of Legazpi City collects the demographic data of the resident per barangay in determining malnutrition in city. A Data Analytics tool was used in extracting, classifying, analyzing and evaluating data that may cause malnutrition in the city. In the results, it shows that the attribute location under the coastal area is more significant in determining the malnutrition in the city. From these findings, the correlation analysis of the data shows that the malnutrition in the city of Legazpi has decreased by 0.24% over-all. However, in the coastal area increases by 0.3%. It is also show in the prediction analysis that the coastal area is significant to the malnutrition. The paper will lead to the Local Government Unit in addressing the factor of malnutrition increase and implement programs which are actually needed in solving the problem.