Evaluation of obesity risk factors using logistic regression and artificial neural networks

In this study, two widely used techniques in a situation where outcome variable is dichotomous, while classifying observations, logistic regression and artificial neural network are examined. The data from obesity survey which is answered by 12th graders of the Anatolian and State high schools in the province of Gaziemir, İzmir is analyzed by using MATLAB, and of the considered methods the predictive abilities are evaluated. The logistic regression coefficients have been determined by using maximum likelihood method. According to the data from obesity survey, whether each relation between obesity risk factor and the outcome variable is significant or not has been determined by using univariate analysis. In the feed forward neural network, for adjusting connection weights, a backpropogation learning rule has been used.