1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
The efficiency of artificial neural network (ANN) for diagnosis of obesity and hypertension
Paper ID : 1026-ICB10
Authors:
Maryam Moradi *1, Anfal Shamsa2
1Laboratory Science, Medipol University, Istanbul, Turkey
2Laboratory Science, Medipol University, Istanbul, Turkye
Abstract:
Obesity and hypertension are health problems in any society. The aim of this study was to evaluate the sensitivity, specificity and accuracy of artificial neural network (ANN) for the diagnosis of obesity and hypertension. For this study, demographic information about 550 students aged 7-18 years was recorded in the ANN program. The recorded demographic information consisted of 11 input variables and 3 output variables. Input variables included age, sex, weight, height, waist circumference, body mass index, waist-to-height ratio, abdominal obesity, physical activity, genetics, and unhealthy eating behaviors, while output variables included obesity, systolic blood pressure, and diastolic blood pressure. In this study, Levenberg-Marquardt and Conjugate Gradient algorithms were used to training the network. The results showed that the selected neural network with Levenberg-Marquardt algorithm had 17 hidden neurons in the diagnosis of obesity and high diastolic blood pressure, while in the diagnosis of high systolic blood pressure it had 15 hidden neurons. Based on the results of the study, the sensitivity, specificity and accuracy of the network in the diagnosis of diastolic blood pressure were 0.8123, 0.9915 and 0.9713, respectively. While these values were 0.9672, 0.9962 and 0.9818 for obesity and 0.8559, 0.9912 and 0.9843 for systolic blood pressure, respectively. Based on the results of the present study, it can be concluded that ANN designed to diagnose obesity, systolic and diastolic blood pressure with equal accuracy of 96%, 85% and 81%, respectively. Therefore, it can be said that ANN program has high efficiency in diagnosing obesity and hypertension.
Keywords:
Artificial Neural Network; Health; Obesity; Hypertension, Efficiency; Iran.
Status : Paper Accepted (Poster Presentation)