A Comprehensive Study on Skin Disease Classification using ML Algorithms

Skin-related illnesses are a significant worldwide health concern, and early detection is crucial to successful treatment. Traditional methods of diagnosing skin disorders rely on the expertise of dermatologists, which can be expensive and time-consuming. Moreover, in countries where dermatologists are in short supply, access to timely and accurate diagnosis may be restricted. This paper proposes a classification of skin diseases based on machine learning algorithms. The five machine learning approaches used for classification are k-nearest neighbours, random forests, decision trees, Naïve Bayes, and support vector machines. The dataset utilized in this investigation was provided by the dermatology department of Peking Union Medical College Hospital in China. The support vector machine is by far the most accurate of these models, with 91.94% accuracy.