COMPARISON OF CLASSIFIERS FOR THE RISK OF OBESITY PREDICTION AMONG HIGH SCHOOL STUDENTS

Year 2019, Volume: 2 Issue: 1, 15 - 21, 30.06.2019

Emel Kuruoğlu Kandemir , Çağın Kandemir Çavaş , Ayça Efe

Abstract

Obesity, which negatively affects human health, is a
chronic disease due to genetic and living conditions. In this study, it was
aimed to examine the observations with three main techniques: logistic
regression, artificial neural networks and Naive Bayes, where the response
variable was two categories of obese/not obese. Obesity questionnaire data,
that was answered by 504 senior students in three randomly selected high
schools in Gaziemir, Izmir, were analysed, and the predictive competences of
the results of the three methods were evaluated. It was found that obesity is
affected by the mother and father’s being obese and eating too much fruit. In
addition, gender and diet status were significantly related with the obesity
risk.

 

In the
artificial neural network, backward propagation learning algorithm was used as
the learning rule in the adjustment of the connection weights according to the
output. With the Naive Bayes method, a classification based on the probability
values ​​of the data was performed. The logistic regression model coefficient
values ​​were determined, using the maximum likelihood method. According to
obesity questionnaire data, it was determined whether the relationship of each
obesity risk factor with the response variable was statistically significant.
The Naive Bayes method has the highest accuracy in prediction obesity compared
to the other two methods.

Keywords

Logistic regression, artificial neural networks, Naive Bayes classification, obesity

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