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An Artificial Neural Network Design For Determination Of Hashimoto’S Thyroiditis Sub-Groups

Author

Listed:
  • Mehmet Emin Aktan

    (Technical University)

  • Erhan Akdoğan

    (Technical University)

  • Namık Zengin

    (Technical University)

  • Ömer Faruk Güney

    (Technical University)

  • Rabia Edibe Parlar

    (Technical University)

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.

Suggested Citation

  • Mehmet Emin Aktan & Erhan Akdoğan & Namık Zengin & Ömer Faruk Güney & Rabia Edibe Parlar, 2016. "An Artificial Neural Network Design For Determination Of Hashimoto’S Thyroiditis Sub-Groups," CBU International Conference Proceedings, ISE Research Institute, vol. 4(0), pages 756-762, September.
    • Handle: RePEc:aad:iseicj:v:4:y:2016:i:0:p:756-762
    DOI: 10.12955/cbup.v4.845
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