Recursive Feature Elimination with Ridge Regression (L2) Machine Learning Hybrid Feature Selection Algorithm for Diabetic Prediction using Random Forest Classifer.

In day today life, diabetes illness is increasing in count due to the body not able to metabolize the glucose level. The prediction of the right diabetes patients is an important research area that many researchers are proposing the techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection is one of the key concept in preprocessing so that the features that are relevant to the disease will be used for prediction. This will improve the prediction accuracy. Selecting right features among the whole feature set is a complicated process and many researchers are concentrating on it to produce the predictive model with high accuracy. In this proposed work, the wrapper based feature selection method called Recursive Feature Elimination (RFE) is combined with Ridge regression (L2) to form a hybrid L2 regulated feature selection algorithm to overcome the overfilling problem of the data set. Over fitting is the major problem in feature selection which means that the new data are not fit to the model since the training data is small. Ridge regression is mainly used to overcome the overfitting problem. Once the features are selected using the proposed feature selection method, random forest classifier is used to classify the data based on the selected features. The proposed work is experimented in PIDD data set and the evaluated results are compared with the existing algorithms to prove the accuracy effect of the proposed algorithm. From the results obtained by proposed algorithm, the accuracy of predicting the diabetes disease is high compared to other existing algorithms.