Open Access

ARTICLE

Diabetes Prediction Algorithm Using Recursive Ridge Regression L2

by Milos Mravik¹, T. Vetriselvi², K. Venkatachalam^3,*, Marko Sarac¹, Nebojsa Bacanin¹, Sasa Adamovic¹

1 Department of Computer Science, Singidunum University, Belgrade, 11000, Serbia
2 Department of Computer science and Engineering, K.Ramakrishnan College of Technology, Trichy, 621112, India
3 Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, 500 03, Hradec Králové, Czech Republic

* Corresponding Author: K. Venkatachalam. Email:

Computers, Materials & Continua 2022, 71(1), 457-471. https://doi.org/10.32604/cmc.2022.020687

Received 03 June 2021; Accepted 07 August 2021; Issue published 03 November 2021

Abstract

At present, the prevalence of diabetes is increasing because the human body cannot metabolize the glucose level. Accurate prediction of diabetes patients is an important research area. Many researchers have proposed techniques to predict this disease through data mining and machine learning methods. In prediction, feature selection is a key concept in preprocessing. Thus, the features that are relevant to the disease are used for prediction. This condition improves the prediction accuracy. Selecting 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 accuracy. In 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 set. Overfitting is a major problem in feature selection, where the new data are unfit to the model because the training data are small. Ridge regression is mainly used to overcome the overfitting problem. The 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 features. This 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 algorithm. The accuracy of the proposed algorithm in predicting diabetes is 100%, and its area under the curve is 97%. The proposed algorithm outperforms existing algorithms.

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Keywords

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