The Classification of Obesity Disease in Logistic Regression and Neural Network Methods

Authors:
Uçman Ergün
Affiliations:
Department of Electronics Engineering, Faculty of Engineering, Afyon Kocatepe University, Afyonkarahisar, Turkey
Venue:
Journal of Medical Systems
Year:
2009

Citing 5
Cited 0

Neural computing: an introduction

Neural computing: an introduction
Modeling obesity using abductive networks

Computers and Biomedical Research
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Classification of Transcranial Doppler Signals Using Artificial Neural Network

Journal of Medical Systems
Compensatory fuzzy neural networks-based intelligent detection of abnormal neonatal cerebral Doppler ultrasound waveforms

IEEE Transactions on Information Technology in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 multi-layer 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.