Thyroid gland influences the metabolic processes of human body due to the fact that it produces hormones. Hyperthyroidism in caused due to increase in the production of thyroid hormones. In this paper a methodology using an online ensemble of decision trees to detect thyroid-related diseases is proposed. The aim of this work is to improve the diagnostic accuracy of thyroid disease. Initially, feature rejection method is applied to discard 10 irrelevant and redundant features from 29 features. Then, it's shown that the offline ensemble of decision trees provides higher performance than state-of-the-art methodologies. Afterwards, the exponential weights based online ensemble method is implemented which reaches comparable classification performance with offline methodology. The proposed system consists of three stages: feature rejection, training decision trees with different cost schemes and the online classification stage where each classifier is weighted using an exponential weight based algorithm. The performance of online algorithm increases as the number of samples increases, because it continuously updates the weights to improve accuracy. The achieved classification accuracy proves the robustness and effectiveness of online version of proposed system in thyroid disease diagnosis.
Özetçe-Bu bildiride Agırlıklı Ortalamalı Tahminci (Weighted Average Forecaster) algoritmasının baglamsal varyantına dayanan yeni bir çevrimiçi sınıflandırma algoritması önerilmiştir. Veri kümesini çevrimiçi olarak baglamlara göre bölümleyen bu algoritma, uzmanlardan gelen önerilerin agırlıklarını baglama göre güncelleyerek, uzman önerilerinin baglamsal füzyonunu saglamaktadır. Önerilen algoritma, UCI veri deposunda mevcut bulunan hastalık verileri üzerinde denenmiştir. Elde edilen sonuçlar, tıbbi teşhis alanında, önerilen sistemin performans ve düşük hesaplama maliyeti açısından saglamlık, etkililik ve çok yönlülügünü kanıtlamaktadır.
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