Finding Nutritional Deficiency and Disease Pattern of Rural People Using Fuzzy Logic and Big Data Techniques on Hadoop

Abstract

Over the decades there is a high demand of a tool to identify the nutritional needs of the people of Bangladesh since it has an alarming rate of under nutrition among the countries of the world. This analysis has focused on the dissimilarity of diseases caused by malnutrition in different districts of Bangladesh. Among the 64 districts, there is no single one found where people have grown proper nutritional food habit. Low income and less knowledge are the triggering factors and the case is worse in the rural areas. In this research, a distributed enumerating framework for large data set is processed in big data models. Fuzzy logic has the ability to model the nutrition problem, in the way helping people to calculate the suitability between food calories and user’s profile. A Map Reduce-based K-nearest neighbor (mrK-NN) classifier has been applied in this research in order to classify data. We have designed a balanced model applying fuzzy logic and big data analysis on Hadoop concerning food habit, food nutrition and disease, especially for the rural people.