Determination of Thyroid Gland State in referrals from Ahvaz University Jah ad laboratory: using Multilayer Perceptron Neural Network Discrimination in Comparing with Classical Discrimination Methods

authors:

avatar Ferdos Mohamadi Basatini 1 , * , avatar Zahra Chinipardaz 2 , avatar Maryam Seyed Tabib 3

Instructor, Department of mathematic, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran
Dental Student , School of Dentistry,Tehran University of Medical Sciences, Tehran, Iran
Master Student of Biological Statistic, School of Public Health, Institute of Public Health Research, Tehran University of Medical Sciences, Tehran, Iran
Jentashapir Journal of Health Research: Vol.4, issue 1; e94065
published online: February 01, 2013
article type: Research Article
received: May 19, 2019
accepted: July 29, 2012

how to cite: Mohamadi Basatini F, Chinipardaz Z, Seyed Tabib M. Determination of Thyroid Gland State in referrals from Ahvaz University Jah ad laboratory: using Multilayer Perceptron Neural Network Discrimination in Comparing with Classical Discrimination Methods. Jentashapir J Cell Mol Biol. 2013;4(1):e94065. 

Abstract

Background: One of the main objects in medical science is diagnosis the diseases and classification the patients to different classes. Consequently according to the above the patients that set in one classes should have maximum similarity with each other. Discrimination and classification analysis have been frequently used in medical data for diagnosis and prognostic of the disease. The wrong in the medical diagnosis is very important, hence the decrease of wrong diagnosis in the discrimination methods are to consider always. Different methods have been used for classification and discrimination of medical data for years. The intention of this research is determination the state of tiroid gland using linear, quadratic and logistic discrimination in camper with the most up-to-date method neural network discrimination. Methods: A total of 225 patients’ data from Jahad university laboratory has analyzed. The obtained data correlated to November 2005. Using spluss/2000 software the collected information proceeds within four methods.
Results: The consequences revealed that linear discrimination misdiagnosis with training set and test set were 0.14 and 0.213 respectively. However these figures considered by quadratic discrimination were 0.053 and 0.106 correspondingly. The obtained records through logistic discrimination for both training set and test set was 0.026 and 0.026. Yet these facts for neural network discrimination recorded in 0.02 and 0.013 likewise.
Conclusion: The neural network discrimination had possibly superior decrease in wrong diagnosis and with notice to the fact that the method need a very fewer statistical assumption, this method is proposed

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References

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