Overview Stats Comments Citations 7 New References (21) Share More spotlight-branding-logo Research Spotlight Beta Want to get 4x more reads of your article? Showcase your recent work in a Spotlight to get 4x more reads on average. Learn more Create Spotlight Abstract Skin disorders are a serious worldwide public health issue that affects a large number of individuals. In recent years, with the fast advancement of technology and the use of different data mining approaches, treatment of skin predictive classification has really become highly predictive as well as accurate. As a result, the type of machine learning approaches capable of efficiently differentiating skin condition categorization is essential. So far, no one machine learning approach has outperformed the others in terms of skin disease prediction. In this research, we introduce a new method that combines two separate data mining approaches into a single unit, as well as an ensemble approach that combines both data mining techniques into a single group. We explore different data mining strategies to categorize the skin condition using an informative Dermatology publicly accessible dataset ISIC2019 images, and then apply an ensemble deep learning method. Furthermore, the presented ensemble technique, which is based on machine and deep learning, was tested on Dermatology datasets and was able to categorize skin disorders into seven categories.