Contributing factors and consequences of childhood obesity – An analysis using Induced Fuzzy Cognitive Maps ( IFCMs )

Introduction Obesity is the most important nutritional disorder of the industrialized world and is also creeping slowly but steadily into the developing countries. Childhood obesity is a complex disease with different genetic, metabolic, environmental and behavioral components that are interrelated and potentially confounding, thus making casual pathways difficult to define[1]. Genetic factors may impart susceptibility to obesity but cannot alone be responsible for epidemic of obesity in such a short span[14]. Environment factors like increase in the availability and marketing of foods, increase in the use of computers and television viewing , greater reliance on motor vehicles for transport, reduction in physical education in schools and physical activity at work are all major determinants of obesity [1,2 ]. Behavioral factors like high intake of food, low physical activity, excessive sugar intake by soft drink [ 1,2] have been playing major roles in the rising rate of obesity. Although genetics are undeniably important in obesity genesis, they form part of a complex interaction with many other environmental, behavioral and metabolic factors. Birth weight, timing or rate of maturation and gestational diabetes are all contributed to the onset of obesity. It has also been established that social factors like ethnicity and social and economic deprivation[2,3] are important determinants of obesity. The current study examines the contributing factors of childhood obesity and analyses the most impactful factor of childhood obesity using Induced Fuzzy Cognitive Maps(IFCMs). An efficient knowledge-based approach utilizing the method of Fuzzy Cognitive Maps(FCMs) is presented in this research work. Fuzzy Cognitive Maps (FCMs) is a well established technique for prediction and decision making especially for situations where fuzziness and uncertainty exists. To deal imprecise information, Lofti A.Zadeh,1965, introduced the notion of fuzziness. In 1986, Kosko [8], the guru of fuzzy logic introduced the Fuzzy Cognitive Maps. It was a fuzzy extension of the Cognitive Map pioneered in 1976 by Political Scientist Robert Axelrod, who used it to represent knowledge as an interconnected, directed, bilevel-logic graph[4]. Thus the FCM plays a vital role in modeling system. This paper describes the method of analyzing the most contributing factor in childhood obesity using Induced Fuzzy Cognitive Maps(IFCMs) which is the advanced study of FCM[15]. It is worth mentioning here, the book entitled ‘FCMs and Neutrosophic Cognitive Maps’ by Vasantha and Smarandache, 2003[16]. This book infers that FCMs strongly resemble neural networks and powerful for reaching consequences as a mathematical tool for modeling complex systems. Implications for interdisciplinary reading: National Implication by Calais[7], FCM based tool for prediction of infectious diseases by Elpiniki et al.,[7], Benefits of literacy in Bhutan by Devadoss et al. [18], Problem faced by bonded labourers near Kodaikanal forests discussed and solution given by Vasantha [17] are notable studies in this area of research. In all the above studies, the various real life with imprecise information taken and the precise solutions given by FCM and its advanced studies. In the current study, Section1 overviews the Fuzzy Cognitive Maps theory, and its influence. Section2 explains the Algorithmic approach of IFCM models. Section3 discusses the possible components(attributes) contributes the Childhood Obesity. Section4 gives implementation of IFCM model and Section5 reveals the discussion of the proposed work. Fuzzy Cognitive Maps Fuzzy Cognitive Maps(FCMs) are digraphs that capture the cause/effect relationship in a system. Nodes of the graph stand for the concepts representing the key factors and attributes of the modeling system, such as inputs, variable states, components,