An entropy based classification of body fat using fuzzy rules commingled with genetic algorithm

Abstract

A perennial inflated malady under the radar which has become an imperative thrust of colloquies in various parts of the world is “Obesity”. It is a health gremlin which curtails a person’s quotidian lifestyle and stems from the physical apathetic torpor, phrenic nerves, deleterious food habits and the frailty of genetic constitution. Bariatrics is that discipline of science which deals with the surgical procedures and consultations for obese individuals. Prolific research and anatomizations have been carried out on the sundry tenacious health impediments germinating due to obesity. The assaying of body fat percentage of every individual in a punctilious method is a desideratum. The previous work entailing body fat percentage comprehensively included an individual’s Body Mass Index (BMI) with respect to their age and gender. The factual composition of a person’s fat constitution and the muscle tissue configuration is not computationally explicated using BMI. Thus, the strictures imposed by the formula vitiates the veracity for an individual having more muscle mass than fat mass. The proposed indagation gives a pellucid, novel formula which is procured through the integrant crude parameters of an individual. This aids in overcoming the fallibilities of the previous formula for body fat. The classification accuracy is augmented by implementing fuzzy rules synthesized into genetic algorithm. The Ethical Committee approval for this study has been obtained from the Institutional Ethics Committee, Madras Medical College, Chennai. The empirical study has been simulated using Matlab and the results have been successfully acquired in the GUI mode.