Analysis of Recursive Feature and Stochastic Gradient Boosting to Predict Skin Cancer

The malignancy of the skin is the most prevalent type of cancer in the worldwide population. It makes it easier to find and treat the illness early, which is a crucial aspect in raising the chances of survival for patients. ??w?v?r, Skin Cancer Predicti?n from Skin Lesions is not a typical problem due to ni?h dimension and the number of factors of many redundant or irrelevant features. Conventional procedures for diagnosis and simple machine learning approaches usually take up more computing power, which makes them incorrect. To overcome these problems, this study introduces a machine learning-based skin cancer prediction (SCP) model based on Stochastic Gradient Boosting (SGB) via Recursive Feature Elimination (RFE). Remove the unimportant and unnecessary data to work on important features only. It is called RFE, which is more than minus importance. At this point, we trained these features on a Stochastic Gradient Boosting Model. This trains multiple weak learners, which are then combined in a single strong predictive model that aims to improve the accuracy of predictions. Overall, the introduced system has better prediction in a less complicated manner and provides more reliable results. This will aid healthcare professionals in better detecting skin cancer in the early stages, leading to better decision-making. Clinical data-based cancer prediction is important for facilitating correct diagnosis and treatment.