Accuracy Measurements and Decision Making by Naïve Bayes and Forward Chaining Method to Identify the Malnutrition Causes and Symptoms

Abstract

Malnutrition is characterized as muscle weakening and cognitive disparity caused by social, dietary, political, food security issues. It appears as many underlying symptoms like fatigue, weakness, micronutrient deficiencies, weight loss to apparent symptoms of muscle mass reduction. Every 1 in 5 children is malnourished in developing countries. Purpose: Policies and program formulation require prevalence facts to classify the most prevalent cause. Diagnostic tools and computer modeling have revolutionized the world of health sciences. Much algorithmic formulation can help to predict the prognosis of diseases based on the previous fact sheets. Methods/Study design/approach: Naïve Bayes provides the posterior probability value that gives an analysis of the member with the whole sample set. Forward chaining gives the logistic conclusion with IF and THEN approach. Result/Findings: Naïve Bayes provided high accuracy of 88% as compared to 85% forward chaining. Novelty/Originality/Value: In this study, the Naïve Bayes algorithm approach is coupled with the forward chaining system to provide a highly accurate measurement of the cause of malnutrition.