Group Penalized Logistic Regressions Predict Ovarian Cancer

Abstract

Objectives: Ovarian cancer ranks first among gynecological cancers in terms of the mortality rate. Accurately diagnosing ovarian benign tumors and malignant tumors is of immense important. The goal of this paper is to combine group LASSO/SCAD/MCP penalized logistic regression with machine learning procedure to further improve the prediction accuracy to ovarian benign tumors and malignant tumors prediction problem. Methods: We combine group LASSO/SCAD/MCP penalty with logistic regression, and propose group LASSO/SCAD/MCP penalized logistic regression to predict the benign and malignant ovarian cancer. Firstly, we select 349 ovarian cancer patients data and divide them into two sets: one is the training set for learning, and the other is the testing set for checking, and then choose 46 explanatory variables and divide them into 11 different groups. Secondly, we apply the training set and group coordinate descent algorithm to obtain group LASSO/SCAD/MCP estimator, and apply the testing set to compute confusion matrix, accuracy, sensitivity and specificity. Finally, we compare the prediction performance for group LASSO/SCAD/MCP penalized logistic regression with that for artificial neural network (ANN) and support vector machine (SVM).Results: Group LASSO/SCAD/MCP/ penalized logistic regression selects 6/4/1 groups. The prediction accuracy and AUC for group MCP/SCAD/LASSO penalized logistic regression/SVM/ANN is 93.33%/85.71%/82.26%/74.29%/72.38% and 0.892/0.852/0.823/0.639/0.789, respectively.Conclusions: Group MCP/SCAD/LASSO penalized logistic regression performs than SVM and ANN in terms of prediction accuracy and AUC. In particular, group MCP penalized logistic regression predicts the best. Therefore, we suggest group MCP penalized logistic regression to predict ovarian tumors.

Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI

“…One study [29] introduced a group-penalized LR model to predict ovarian cancer. The authors combined group SCAD/LASSO/MCP-penalized LR with an ML model to enhance accuracy.…”

“…In the same vein, in ref. [29], experiments were performed using LR with different penalties such as LASSO, SCAD, MCP, etc., along with ANN and SVM models. No validation was carried out in this study and performance was not compared with stateof-the-art models.…”

“…For instance, in [28] the authors employed LR with LASSO and used 30 features of the dataset, which resulted in an accuracy score of 90.6%. In [29], the MCP models were used for ovarian cancer detection, achieving an accuracy of 93.33%, but they utilized only 46 features from the dataset. Similarly, in [54] the authors applied RF for ovarian cancer using the OC Marker dataset and achieved 91% accuracy.…”