The classification of obesity disease in logistic regression and neural network methods.
The aim of this study is to establish an automated system to recognize and to follow-up obesity. In this study, the areas affected from obesity were examined with a classification considering the divergent arteries and body mass index of 30 healthy and 52 obese people by using two different mathematical models such as the traditional statistical method based on logistic regression and a multilayer perception (MLP) neural network, and then classifying performances of logistic regression and neural network were compared. As a result of this comparison, it is observed that the classifying performance of neural network is better than logistic regression; also the reasons of this result were examined. Furthermore, after these classifications it is observed that in obesity the body mass index is more affected than the divergent arteries.
Ergün
Uçman
U
Department of Electronics Engineering, Faculty of Engineering, Afyon Kocatepe University, Afyonkarahisar, Turkey. uergun@aku.edu.tr
D015992:Body Mass Index; D016022:Case-Control Studies; D006801:Humans; D016015:Logistic Models; D016571:Neural Networks, Computer; D009765:Obesity / Q000145:classification* / Q000175:diagnosis; D012372:ROC Curve