SavedAdd your journal

Journals / Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik / 2014 / Cilt: 15 - Sayı: 1

TİROİT HASTALIKLARIN TEŞHİSİ İÇİN AŞIRI ÖĞRENME MAKİNESİ TABANLI UZMAN BİR TANI SİSTEMİ

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

Yılmaz KAYA
Pages
41–49
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The classification accuracy obtainedthrough the ELM is 96.79% for 70-30% training-test partition.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With iodine taken from outside, the thyroid gland is an organ that secretes hormones calledthyroxin.
📊
Key Findings & Data:All metabolic functions of human beings are controlled by these hormones.
💡
Conclusion & Impact:The classification accuracy obtainedthrough the ELM is 96.79% for 70-30% training-test partition.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

With iodine taken from outside, the thyroid gland is an organ that secretes hormones calledthyroxin. All metabolic functions of human beings are controlled by these hormones. An overactivethyroid 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 causeshypothyroidism. The diagnosis of thyroid gland disorders by assessing the data of thyroid in clinicalapplications comes out as an important classification problem. In this study, Extreme LearningMachine (ELM) was applied to the thyroid data set taken from UCI machine learning repository. TheELM is single hidden layer feed-forward artificial neural network model which can be learnt fast. Itwas seen that the ELM, for the data set, has the upper hand in terms of both classification accuracy andspeed when compared to other machine learning methods. The classification accuracy obtainedthrough the ELM is 96.79% for 70-30% training-test partition.

Özet

Tiroit bezi dışarıdan alınan iyot minerali ile tiroksin denilen hormonları yapan bir organdır.İnsana ait tüm metabolizma faaliyetleri bu hormonları tarafından kontrol edilmektedir. Bu hormonlarınaşırı salınması hyperthyroidism, az salınması ise hypothyroidism bozuklarının ortaya çıkmasına nedenolmaktadır. Klinik uygulamalarda tiroit verilerin yorumlanarak tiroit bezi bozukluğu tanısınınkonulması önemli bir sınıflandırma problemi olarak karşımıza çıkmakta. Bu çalışmada UCI makineöğrenmesi veri tabanından alınan tiroit veri setine aşırı öğrenme makinesi (AÖM) yöntemiuygulanmıştır. 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 başarısıhem de hız bakımından önemli avantajlar sağladığı görülmüştür. AÖM ile elde edilen sınıflandırmabaşarısı 96.79 % olarak elde edilmiştir.

🔖

Cite This Article

KAYA, Y. (2022). TİROİT HASTALIKLARIN TEŞHİSİ İÇİN AŞIRI ÖĞRENME MAKİNESİ TABANLI UZMAN BİR TANI SİSTEMİ. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik, Cilt: 15 - Sayı: 1, 41–49. https://ajindex.com/articles/438377

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. Chih-Lin C., W. Nick Street, David A. Katz, “A Decision Support System for Cost- Effective Diagnosis”. Arti ficial Intelligence in Medicine, 50, 149-161, 2010.
  2. Esin D., Akif D., Derya A., “An Expert System Based on Generalized Discriminant Analysis and Wavelet Support Vector Machine for Diagnosis of Thyroid Diseases”, Expert Systems with Applications, 38, 146-150, 2011.
  3. Handoko, S. D., Keong, K. C., Ong, Y. S., Zhang G.L., Brusic V, “Extreme Learning Machine for Predicting HLA- Peptide Binding”, Lecture Notes in Computer, 3973, 716–721, 2006. Hai-Jun R.,Yew-Soon O., Ah-Hwee T., Zexuan Zhu, “A Fast Pruned-Extreme Learning Machine for Classification Problem”, Neurocomputing, 72, 359-366, 2008.
  4. Halife K., Seral O., Arslan,A., Gunes, S., “Medical Application of Information Gain Based Artificial Immune Recognition System (AIRS): Diagnosis of Thyroid Disease”, Expert Systems with Applications, 36, 3086-3092, 2009.
  5. Hoshi, K., Kawakami, J., Kumagai, M., Kasahara, S., Nisimura, N., Nakamura, H., “An Analysis of Thyroid Function Diagnosis using Bayesian-type and SOM- Type Neural Networks”, Chemical and Pharmaceutical Bulletin, 53, 1570-1574, 2005.
  6. Huang, G.B., Zhu, Q.Y., Siew, C.K. Extreme Learning Machine: Theory and Applications. Neurocomputing, 70 (1-3), 489-501, 2006.
  7. Kaya, Y., “A New Intelligent Classifier for Breast Cancer Diagnosis Based on Rough Set and Extreme Learning Machine: RS+ELM”, Turkish Journal of Electrical Engineering and Computer Sciences, 21:2079-2091, 2013.
  8. Kaya, Y., Uyar, M., “A Hybrid Decision Support System Based on Rough Set and Extreme Learning Machine for Diagnosis of Hepatitis Disease”, Applied Soft Computing Journal, 13,3429- 3438, 2013.
  9. Kaya, Y., Kayci, L., Tekin, R., Ertugrul, Ö.F., “Evaluation of Texture Features for Automatic Detecting Butterfly Species using Extreme Learning Machine”, Journal of Experimental & Theoretical Arti ficial Intelligence, DOI:10.1080/0952813X.2013.861875, 2014.
  10. Keles, A., Keles, A. “ESTDD: Expert System for Thyroid Diseases Diagnosis”, Expert Systems with Applications, 34:1, 242-246, 2008.
  11. Liang, N., Y., Saratchandran,P., Huang G., P., Sundararajan, N., “Classification of Mental Tasks From Eeg Signals using Extreme Learning Machine”, International. Journal of Neural Systems, 16 (1), 29-38, 2006.
  12. Ozyılmaz, L. Yıldırım, T., “Diagnosis of Thyroid Disease using Artificial Neural Network Methods”, In Proceedings of The 9th International Conference on Neural Information Processing (ICONIP’02), 4, 2033-2036, 20