Journal of Clinical & Experimental Dermatology Research
Open Access

Abstract

Diagnosis of Erythemato-Squamous Skin Diseases by Machine Learning Algorithms

Mustafa Necati Bozok* and Ali Çalhan

Since erythema squamous skin diseases show very close findings in clinical examination, a biopsy is taken from the patient for definitive diagnosis and the diagnosis of the disease can be made according to the biopsy result. On literature, classification studies were carried out on these diseases using machine learning and classification methods. Researches were mostly focused on optimizing and reducing database features for better classification score. Due to importance of reflecting specifications of diseases we especially focused on dataset features named as clinic or histopathological features findings. In this study, histopathological features of diseases were discussed first and then we developed an algorithm to remove outlier data from the dataset. This algorithm leads us to discover a threshold value to achieve better outlier removal. Logistic regression, KNeihgbors classifier, Support vector classifier, Gaussian naive bayes, Decision tree classifier and Random forest classifier methods applied to the outlier free dataset. It was determined that the Gaussian Naive Bayes method was the most appropriate classification method with 100% score. The results we obtained as a result of the algorithm we developed, being compatible with the clinical and histopathological features of skin diseases with erythema squamous, is a positive result for this study.

Published Date: 2022-08-22; Received Date: 2022-07-19