Advanced feature selection methodology for cancer datasets to improve accuracy of classification
In cancer disease, there are four stages in which first two stages are called as early stage and the last two stages are known as last stages. As we know the cancer is a non-curable disease if we diagnose at the last stages and it is also not an easy task to diagnose the cancer at early stages also. For the early stage diagnosis, we are can apply machine learning techniques. Machine Learning is one of the trending domains in the computer science discipline and also machine learning algorithms are so effective for analyzing the biological datasets. By the help of machine learning methodologies, we can analyze the cancerous tumors of a human body with high accuracy. In this type of prediction type of model, we can have some issues related to overfitting model. In which, we are having less priority features to be removed. There will be complexity in computational access while eliminating low priority features and also some high priority features. Hence, in this research work we are focusing on few numbers of high priority features and predicting the cancer more accurately.