A FAST INTELLIGENT DIAGNOSIS SYSTEM FOR THYROID DISEASES BASED ON EXTREME LEARNING MACHINE

Year 2014, Volume: 15 Issue: 1, 41 - 49, 05.05.2015

Yilmaz Kaya

https://doi.org/10.18038/btd-a.89202

Cited By: 1

Abstract

With iodine taken from outside, the thyroid gland is an organ that secretes hormones called thyroxin. All metabolic functions of human beings are controlled by these hormones. An overactive thyroid gland which is producing an excessive amount of these hormones causes hyperthyroidism, while an underactive thyroid gland that is not producing enough of these hormones causes hypothyroidism. The diagnosis of thyroid gland disorders by assessing the data of thyroid in clinical applications comes out as an important classification problem. In this study, Extreme Learning Machine (ELM) was applied to the thyroid data set taken from UCI machine learning repository. The ELM is single hidden layer feed-forward artificial neural network model which can be learnt fast. It was seen that the ELM, for the data set, has the upper hand in terms of both classification accuracy and speed when compared to other machine learning methods. The classification accuracy obtained through the ELM is 96.79% for 70-30% training-test partition.

 

Keywords

Extreme Learning Machine, Thyroid Diseases, Machine Learning, Expert System

ANADOLU ÜNİVERSİTESİ

Abstract

Tiroit bezi dışarıdan alınan iyot minerali ile “tiroksin” denilen hormonları yapan bir organdır. İnsana ait tüm metabolizma faaliyetleri bu hormonları tarafından kontrol edilmektedir. Bu hormonların aşırı salınması hyperthyroidism, az salınması ise hypothyroidism bozuklarının ortaya çıkmasına neden olmaktadır. Klinik uygulamalarda tiroit verilerin yorumlanarak tiroit bezi bozukluğu tanısının konulması önemli bir sınıflandırma problemi olarak karşımıza çıkmakta. Bu çalışmada UCI makine öğrenmesi veri tabanından alınan tiroit veri setine aşırı öğrenme makinesi (AÖM) yöntemi uygulanmıştır. AÖM hızlı öğrenebilen tek gizli katmanlı ileri beslemeli bir yapay sinir ağ modelidir. Ele alınan veri seti için AÖM, diğer makine öğrenmesi yöntemlere göre hem sınıflandırma başarısı hem de hız bakımından önemli avantajlar sağladığı görülmüştür. AÖM ile elde edilen sınıflandırma başarısı 96.79 % olarak elde edilmiştir

Keywords

Aşırı Öğrenme Makinesi, Tiroit Hastalıklar, Makine Öğrenmesi, Uzman Sistem

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