Diagnosing Thyroid Disease by Neural Networks
Mohammad reza obeidavi1, Ali rafiee1 and omid mahdiyar2

1Department of Biomedical Engineering, Kazeron Branch, Islamic Azad University, Kazeron, Iran,

2Department of Electrical Engineering, Kazeron Branch, Islamic Azad University, Kazeron, Iran.

Corresponding Author E-mail: Reza.obeidavi@yahoo.com

Abstract: The aim of the present study is to diagnose the types of thyroid disease, using neural networks. In this research, the tests (T3UR, FTI, FT4, FT3, T4, T3, TSH) were conducted on 244 subjects (30 subjects with healthy thyroid, 30 subjects suffering from hyperthyroidism, 34 subjects suffering from hypothyroidism, 30 subjects suffering from Subclinical hyperthyroidism and 30 subjects suffering from Subclinical hypothyroidism , 30 subjects suffering from hyperthyroidism treatment and 30 subjects suffering hyperthyroidism treatment, and 30 subjects with resistant thyroid) for investigating status of their thyroid by considering their age and diversity of normal range of hormone tests in various ages. Data analysis was conducted using MATLAB 2014 software to categorize the thyroid disease. For investigating authenticity of the data, by using 3-fold cross-validation, authenticity of categorization of thyroid disease was evaluated by neural networks (MLP, PNN, GRNN, FTDNN, CFNN). In this method, networks are taking 7 hormone tests and age as input, output is diagnosed with thyroid disease. Also for 40 subjects (5 subjects for each categories), new data were given to the GUI of MATLAB through the designed graphical user interface for testing the network, and all 40 data were correctly responded. Results of this research indicated that by hormone tests and using neural networks, various types of thyroid disease can be diagnosed and the neural network provides us with almost 100% correct answers.

Keywords: Neural networks; thyroid disease; hormone tests; Matlab

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