(untitled) (International Journal of Engineering and Computer Science, 2014)
In today’s modern world, Globalization, demographic transition, life style changes and dietary meal patterns influences people’s nutrition. This work attempts to demonstrate the analysis of malnutrition based on food intakes, wealthy index, age group, education level, occupation, etc. Objective of this work is to use of effective supervised machine learning techniques-decision trees and artificial neural networks to classify dataset of family health survey and Classification and prediction techniques provides appropriate and flexible methods to process large amount of data for specifying accurate malnutrition detection and prevention over the survey dataset. The result of supervised data mining techniques in nutrition database provides the nutrition status of children age under five. This work is useful to improve nutrition level of public health with the help of government health services to the people.