Structured Ranking Method-based Feature Selection in Data Mining

Abstract: Health diseases have been issued seriously harmful in
human life due to different dehydrated food and disturbance of
working environment in the organization. Precise prediction and
diagnosis of disease become a more serious and challenging task for primary deterrence, recognition, and treatment. Thus, based on the above challenges, we proposed the Medical Things (MT) and machine learning models to solve the healthcare problems with appropriate services in disease supervising, forecast, and diagnosis. We developed a prediction framework with machine learning approaches to get different categories of classification for predicted disease. The framework is designed by the fuzzy model with a decision tree to lessen the data complexity. We considered heart disease for experiments and experimental evaluation determined the prediction for categories of classification. The number of decision trees (M) with samples (MS), leaf node (ML), and learning rate (I) is determined as MS=20, ML=3, I=0.1, then mean test score(m) is 20.