Group penalized logistic regression differentiates between benign and malignant ovarian tumors

Ovarian cancer is one of the most common types of cancer in women. Correct differentiation between benign and malignant ovarian tumors is of immense importance in medical fields. In this paper, we introduce group penalized logistic regressions to enhance diagnosis accuracy. Firstly, we divide 349 ovarian cancer patients into two sets: one for learning model parameters, and the other for assessing prediction performance, and select 46 variables from 49 traits as the predictor vector to construct GLASSO/GSCAD/GMCP penalized logistic regressions with 11 groups. Secondly, we develop group coordinate descent (GCD) algorithm and its specific pseudo code to simultaneously complete group selection and group estimation, introduce the tenfold cross validation (CV) procedure to select the relatively optimal tuning parameter, and apply the testing set and Youden index to obtain class probability estimator and class index information. Finally, we compute the accuracy, precision, specificity, sensitivity, F1-score and the area under ROC curve (AUC) to assess the prediction performance to the proposed group penalized methods, and found that GLASSO/GSCAD/GMCP penalized logistic regressions outperform three machine learning methods (ANN, artificial neural network; SVM, support vector machine; XGBoost, eXtreme gradient boosting) and three deep learning methods (CNN, convolutional neural network; DNN, deep neural network; RNN, recurrent neural network) in terms of accuracy, F1-score and AUC.