AN ARTIFICIAL NEURAL NETWORK DESIGN FOR DETERMINATION OF HASHIMOTO’S THYROIDITIS SUB-GROUPS

Mehmet Emin Aktan (), Erhan Akdoğan (), Namık Zengin (), Ömer Faruk Güney () and Rabia Edibe Parlar ()
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Mehmet Emin Aktan: Technical University
Erhan Akdoğan: Technical University
Namık Zengin: Technical University
Ömer Faruk Güney: Technical University
Rabia Edibe Parlar: Technical University

CBU International Conference Proceedings, 2016, vol. 4, issue 0, 756-762

Abstract: In this study, an artificial neural network was developed for estimating Hashimoto’s Thyroiditis sub-groups. Medical analysis and measurements from 75 patients were used to determine the parameters most effective on disease sub-groups. The study used statistical analyses and an artificial neural network that was trained by the determined parameters. The neural network had four inputs: thyroid stimulating hormone, free thyroxine (fT4), right lobe size (RLS), and RLS2 – fT44, and two outputs for three groups: euthyroid, subclinical, and clinical. After training, the network was tested with data collected from 30 patients. Results show that, overall, the neural network estimated the sub-groups with 90% accuracy. Hence, the study showed that determination of Hashimoto’s Thyroiditis sub-groups can be made via designed artificial neural network.

Keywords: artificial neural networkshashimoto; thyroiditis; statistical analyze; diagnosis (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:aad:iseicj:v:4:y:2016:i:0:p:756-762

DOI: 10.12955/cbup.v4.845

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