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Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

Computers, Materials & Continua 2022年第4期71卷 457-471页

作者： milos mravik T.Vetriselvi K.Venkatachalam Marko Sarac Nebojsa Bacanin Sasa Adamovic Department of Computer Science Singidunum UniversityBelgrade11000Serbia Department of Computer science and Engineering K.Ramakrishnan College of TechnologyTrichy621112India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

At present,the prevalence of diabetes is increasing because the human body cannot metabolize the glucose *** prediction of diabetes patients is an important research *** researchers have proposed techniques to predict this disease through data mining and machine learning *** prediction,feature selection is a key concept in ***,the features that are relevant to the disease are used for *** condition improves the prediction *** the right features in the whole feature set is a complicated process,and many researchers are concentrating on it to produce a predictive model with high *** this work,a wrapper-based feature selection method called recursive feature elimination is combined with ridge regression(L2)to form a hybrid L2 regulated feature selection algorithm for overcoming the overfitting problem of data *** is a major problem in feature selection,where the new data are unfit to the model because the training data are *** regression is mainly used to overcome the overfitting *** features are selected by using the proposed feature selection method,and random forest classifier is used to classify the data on the basis of the selected *** work uses the Pima Indians Diabetes data set,and the evaluated results are compared with the existing algorithms to prove the accuracy of the proposed *** accuracy of the proposed algorithm in predicting diabetes is 100%,and its area under the curve is 97%.The proposed algorithm outperforms existing algorithms.

关键词： Ridge regression recursive feature elimination random forest machine learning feature selection

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