Abstract
There is a tremendous increase in severe cases of type 2 diabetes in the day today's life. Therefore, proper assessment of the disease is critical to saving society. Many prediction models help identify type 2 diabetes. At the same time, every model varies based on the performance measures. Various kinds of algorithms such as Decision Tree, Logistic Regression, KNN, Random Forest algorithm are applied to identify type 2 diabetes. At this juncture, used the implementation of type 2 Classification by AdaBoost algorithms, an ensemble approach. Here, the proposed methodology of the paper is to implement an ensemble approach of machine learning to receive a better efficiency compared to other existing algorithms for the classification of type 2 diabetes. When compared to all different algorithms, this ensemble approach shows an efficiency of 83%. The accuracy is calculated based on various performance measures.
Classification of P300 Component Using a Riemannian Ensemble Approach
