Classification of Erythematosquamous Dermatosis by the Method of Random Forest

Machine Learning (ML) methods have found wide applications in dermatology (Chan et al., 2020) [1]. Thomsen, Iversen, Titlestad & Winther (2020) reviewed 2175 publications and found that the most common usage of ML methods was in the binary classification of malignant melanoma from images [2]. Adamson and Smith have a word of advice about usage of ML methods in diagnosis of skin diseases that inclusivity must be kept in mind for classification results to be accurate [3]. Steele et al. searched PubMed, Embase, and CENTRAL, and found that the performance of ML methods was variable, and overall accuracy measure was not a good measure for sub-group accuracy [4].