Comparison of Artificial Neural Networks with Logistic Regression for Detection of Obesity

• DOI: 10.1007/s10916-011-9711-4
• Publication Date: 2011-05-09T11:21:14Z
• Authors: Seyed Taghi Heydari; Seyed Mohammad Ta...; Najaf Zare
• Keywords: Adult; 2. Zero hunger; Middle Aged; Young Adult; 03 medical and health sciences; Cross-Sectional Studies; Logistic Models; 0302 clinical medicine; ROC Curve; Confidence Intervals; Odds Ratio; Humans; Neural Networks, Computer; Obesity

Obesity is a common problem in nutrition, both in the developed and developing countries. The aim of this study was to classify obesity by artificial neural networks and logistic regression. This cross-sectional study comprised of 414 healthy military personnel in southern Iran. All subjects completed questionnaires on their socio-economic status and their anthropometric measures were measured by a trained nurse. Classification of obesity was done by artificial neural networks and logistic regression. The mean age±SD of participants was 34.4 ± 7.5 years. A total of 187 (45.2%) were obese. In regard to logistic regression and neural networks the respective values were 80.2% and 81.2% when correctly classified, 80.2 and 79.7 for sensitivity and 81.9 and 83.7 for specificity; while the area under Receiver-Operating Characteristic (ROC) curve were 0.888 and 0.884 and the Kappa statistic were 0.600 and 0.629 for logistic regression and neural networks model respectively. We conclude that the neural networks and logistic regression both were good classifier for obesity detection but they were not significantly different in classification.