Volume 20, Issue 113 (11-2013)                   RJMS 2013, 20(113): 20-28 | Back to browse issues page

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Adeli M, Biglarian A, Bakhshi E, Adeli O A. Application of artificial neural network model in predicting the mixed response of atherosclerosis disease. RJMS 2013; 20 (113) :20-28
URL: http://rjms.iums.ac.ir/article-1-2778-en.html
University of Social Welfare and Rehabilitation Sciences
Abstract:   (6422 Views)

Background: In epidemiological and medical studies, sometimes researchers are faced for prediction of two response variables (simultaneously) based on a number of independent variables. When the response variable is mixed, according to established limits and absence of assumption, the classical statistical methods are not enough efficient for classification and prediction goals. The purpose of this study is using Artificial Neural Network (ANN) model to predict the mixed response variable in heart disease.


Methods: A total of 276 cardiac patients who were discharged from Madani Hospital were studied as historical cohort, from October 2011 to March 2012. This sample was used to predict the cholesterol and also LDL levels of patients. Data was randomly divided into two sets: training (175 cases) and testing (91 cases) sets. Data analysis was made by ANN model with SCG algorithms in MATLAB software, version 7.11 and appropriateness of the model was assessed by the accuracy prediction.


Results: The highest accuracy of prediction of mixed response variable was 51.76% for a four-layer ANN model.


Conclusions: The ANN model is suggested to predict the mixed response variable in medical studies.

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Type of Study: Research | Subject: Biostatistics

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