A Hybrid Medical Decision Making System Based on Principles Component Analysis, k-NN Based Weighted Pre-Processing and Adaptive Neuro-Fuzzy Inference System
Yükleniyor...
Dosyalar
Tarih
2006
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Academic Press Inc Elsevier Science
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Proper interpretation of the thyroid gland functional data is an important issue in diagnosis of thyroid disease. The primary role of the thyroid gland is to help regulation of the body's metabolism. Thyroid hormone produced by thyroid gland provides this. Production of too little thyroid hormone (hypo-thyroidism) or production of too much thyroid hormone (hyper-thyroidism) defines the types of thyroid disease. It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of thyroid disease, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on thyroid disease using principles component analysis (PCA), k-nearest neighbor (k-NN) based weighted pre-processing and adaptive neuro-fuzzy inference system (ANFIS). The proposed system has three stages. In the first stage, dimension of thyroid disease dataset that has 5 features is reduced to 2 features using principles component analysis. In the second stage, a new weighting scheme based on k-nearest neighbor (k-NN) method was utilized as a pre-processing step before the main classifier. Then, in the third stage, we have used adaptive neuro-fuzzy inference system to diagnosis of thyroid disease. We took the thyroid disease dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 100% and it was very promising with regard to the other classification applications in literature for this problem.
Açıklama
Anahtar Kelimeler
Principles component analysis, Anfıs, K-nn based weighted pre-processing, Thyroid disease diagnosis, Hybrid systems
Kaynak
Digital Signal Processing
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
16
Sayı
Künye
Polat, K., Güneş, S., (2006). A Hybrid Medical Decision Making System Based on Principles Component Analysis, k-NN Based Weighted Pre-Processing and Adaptive Neuro-Fuzzy Inference System. Digital Signal Processing, (16), 913-921. Doi: 10.1016/j.dsp.2006.05.001
