Eritematöz Skuamöz Hastalıkların Teşhisinde Makine Öğrenme Algoritmaları Performanslarının Değerlendirilmesi.

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. [ABSTRACT FROM AUTHOR]