Nutrition Deficiency Prediction using Machine Learning Techniques

Abstract

Despite the fact that many developing nations have experienced economic progress, Nutrition- deficiency remains a pervasive problem in the society, with millions of impoverished people's diets lacking in essential macro and micronutrients essential for optimal human health. Lack of awareness of food consumed daily causes Nutrition deficiency among general population, data from multiple health records are used for research and prediction. It investigates the importance of a well-balanced diet for our daily life. The Healthy Food Diversity Index (HFDI) is a supplement to the popular Household Dietary Diversity Score (HDDS). It's a tool for determining the diversity of household food. The HDDS has been established as a reliable source of information, but it has several limitations as a measure of dietary diversity that is linked to nutritional quality.  In this paper, various machine learning techniques such as Random Forest classifier (RF), Support-Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Logistic Regression (LR) are used  to predict Nutrition-Deficiency using house hold risk factors and they compared their Accuracy, Sensitivity and Specificity. The predictions were also compared to the anthropometric classifications used by the National school feeding program to prove the efficiency of the proposed approach.