"Development and Performance Investigations of Classification Algorithm for Skin Melanoma using Random Forest Classifier Approach"

the fourth greatest source of non-fatal diseases affecting 30-70% of the population worldwide is skin disease, and it is common across geography and ages. Skin disease is the predominant form of skin cancer in the United States. Skin disease is one of the most common health problems. In order to provide statistical representations on suspended areas, computer-operated diagnostic systems require sophisticated image processing algorithms. In this article we analyzed the latest scientific progress used for the diagnostic methods for computer-aided skin lesions. The phases include pre-processing of skin lesions, segmentation, collection of features and the recognition of particular characteristics. In this paper we implemented Random Forest (RF) classification algorithm to classify benign and malignant small skin image dataset. In RF classifier Bootstrap aggregation and selection of K and N node is key stage which decide performance of algorithm. Classifier Performance was improved in terms of accuracy (+3.44%), Area under ROC (+1.05%), precision (-0.8%) during cross validation score. Results show that RF classifier is able to achieve classification accuracy 86.90% for malignant cases and similarly 88.67 % for benign cases.