Deep Neural Decision Forests for Accurate ESD Prediction: A Novel Approach

Skin is one of the most versatile and is one of the largest organs in the body. It has many important functions, it regulates the temperature of the body and at the same time also protects our internal organs from external damage. This outer layer shields the body from outside particles including harmful UV rays, bacteria, and chemicals, and other substances. Without proper care and maintenance, the skin can become dry, irritated, and more susceptible to infection and disease. Similar to other bodily organs, the skin needs care and maintenance for its healthy functioning. WHO says that the world’s most widespread health issues are mainly skin conditions, ranking as the third most frequent cause of health issues. Additionally, they are one of the leading causes of disability and are among the most common reasons for visits to outpatient clinics. The World Health Organization has reported a noticeable increase of skin cancer in the last few years, it also identified UV radiation as a leading cause. present a multi-filter deep neural decision forest-based method determining which category the ESD disease belongs to. Data used in this project includes both histopathological and also the clinical attributes of various patients suffering from EDS. The proposed multi-filter feature selection method identifies the most optimal features and attributes contributing to disease detection and improves its performance. After generating the feature set a deep neural decision forest is utilized for determining the category of the disease. To evaluate more on this particular method, various criteria were taken and checked with other conventional classification approaches.