Improving the Diagnosis of Breast Cancer Using Regularized Logistic Regression with Adaptive Elastic Net

Abstract

Early diagnosis of breast cancer helps improve the patient's chance of survival. Therefore, cancer classification and feature selection are important research topics in medicine and biology. Recently, the adaptive elastic net was used effectively for feature-based cancer classification, allowing simultaneous feature selection and feature coefficient estimation. The adaptive elastic net basically employed elastic net estimates as the initial weight. Nevertheless, the elastic net estimator is inconsistent and biased in selecting features. Therefore, the regularized logistic regression with the adaptive elastic net (RLRAEN) was used to handle the inconsistency problem by employing the adjusted variances of features as weights within the L1- regularization of the elastic net model. The proposed method was applied to the Wisconsin Breast Cancer dataset of the UCI repository and compared to the other existing penalized methods that were also applied to the same dataset. Based on the experimental study, the RLRAEN was more efficient in terms of feature selection and classification accuracy than the other competing methods. Therefore, it can be concluded that RLRAEN is a better method in breast cancer classification.