• Mehmet Emin Aktan Technical University
  • Erhan Akdoğan Technical University
  • Namık Zengin Technical University
  • Ömer Faruk Güney Technical University
  • Rabia Edibe Parlar Technical University
Keywords: artificial neural networks, hashimoto, thyroiditis, statistical analyze, diagnosis


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.


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