Eritematöz Skuamöz Hastalıkların Teşhisinde Makine Öğrenme Algoritmalarının Etkisi

Öz

Eritematöz skuamöz hastalıkların ayırıcı tanısı dermatolojide önemli problemlerden biridir. Hepsi birbirleri arasında, çok az farkla eritem ve ölçeklenmenin klinik özelliklerini paylaşmaktadırlar. Bu gruba dâhil olan hastalıklar; sedef hastalığı, seboreik dermatit, liken planus, gül hastalığı (pityriasis rosea), kronik dermatit ve pityriasis rubra pilaris olarak sınıflandırılabilmektedir. Tanı için genellikle biyopsi gereklidir ancak ne yazık ki bu hastalıklar pek çok histopatolojik özelliği de paylaşmaktadır. Diğer taraftan, son yıllarda özellikle bilgisayar teknolojisindeki gelişmeler ve yapay zekâ teknolojileri, biyomedikal alanda kendine geniş bir uygulama alanı bulmuştur. Tıbbi cihazlarda bilgisayar teknolojilerinin kullanılmasıyla daha hassas, daha hızlı, insandan kaynaklanan hataları minimize eden cihazlar geliştirilmektedir. Dolayısıyla, bu çalışmada, makine öğrenme algoritmaları deri hastalıklarının sınıflandırılması ve tahmininde ne kadar etkili olmaktadır onun araştırılması yapılmıştır. Bu çalışmada, 366 hastaya ait 33 nitelikten oluşan deri doku örnekleri, Destek Vektör Makineleri (Support Vector Machines - SVM), Topluluk Öğrenme Algoritmaları (Ensemble Learning Algorithms - ELA), Karar Ağaçları (Decision Trees - DT) ve k-En Yakın Komşuluk (k-Nearest Neighborhood - k-NN) ile sınıflandırılmış ve en yüksek doğruluk değerleri kaydedilmiştir. Buna göre deri hastalıklarının ayrıştırılması ve sınıflandırılması ile ilgili etkiler araştırılmıştır. SVM ile bu veri setinde, önceki tüm çalışmalardan daha yüksek olan %99.73'lük bir doğruluk elde edilmiştir.

Anahtar Kelimeler

• deri hastalıkları
• destek vektör makinesi
• topluluk öğrenme algoritmaları
• karar ağaçları
• k-en yakın komşuluk

Evaluation of Machine Learning Algorithms Performance in Diagnosis of Erythematous Squamous Diseases

Öz

Differential diagnosis of erythematous squamous diseases is one of the important problems in dermatology. They all share the clinical picture of erythema and scaling among each other, with little difference. The diseases included in this group can be classified as psoriasis, seborrheic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris. Biopsy for diagnosis, but unfortunately these diseases also share many histopathological features. Technologies related to some other technologies have found wide application in the biomedical field. With the use of computer technologies in medical devices, more sensitive, faster, faster, cutting devices are developed. Therefore, it has been investigated how effective machine learning algorithms are in classifying and predicting skin diseases. In this study, skin tissue samples consisting of 33 attributes belonging to 366 patients, Support Vector Machines (SVM), Ensemble Learning Algorithms (ELA), Decision Trees (DT), k-Nearest Neighborhood (k-NN) were classified with algorithms and the highest knowledge information was recorded. Accordingly, the effects related to the separation and classification of skin diseases have been investigated. SVM has achieved an accuracy of 99.73% which is higher than all the previous studies on this dataset.

Anahtar Kelimeler

• skin diseases
• support vector machine
• ensemble learning algorithms
• decision trees
• k-nearest neighborhood

Kaynakça

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