Group Penalized Logistic Regressions Predict Ovarian Cancer

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.

Group Penalized Logistic Regressions Predict Ovarian Cancer ∗

Objectives: Ovarian cancer ranks fifirst 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 difffferent 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 specifificity. Finally, we compare the prediction performance for group LASSO/SCAD/MCP penalized logistic regression with that for artifificial 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.

Counting Bounded Elements of a Number Field

We estimate, in a number field, the number of elements and the maximal number of linearly independent elements, with prescribed bounds on their valuations. As a by-product, we obtain new bounds for the successive minima of ideal lattices. Our arguments combine group theory, ramification theory, and the geometry of numbers.

-TRANSITIVE DIGRAPHS PRESERVING A CARTESIAN DECOMPOSITION

In this paper, we combine group-theoretic and combinatorial techniques to study $\wedge$-transitive digraphs admitting a cartesian decomposition of their vertex set. In particular, our approach uncovers a new family of digraphs that may be of considerable interest.

Networks and Groups in Southeast Asia: Some Observations on the Group Theory of Politics

The paper describes a “dyadic” type of political structure which, it is argued, is a necessary supplement to class and interest group models for the analysis of informal political structure in contemporary Southeast Asia, and probably in other developing areas.Various types of simple and complex dyadic structures are described. The paper then examines four Southeast Asian polities, of different degrees of political development, with attention to the manner in which they combine group and dyadic structures. The examples are the Kalinga, a pagan ethnolinguistic group of Northern Luzon; the Tausug, a Muslim group of the Sulu archipelago; the traditional Thai monarchy; and the present Republic of the Philippines. In each case the effects of structure upon the operation of the system are explored. The paper concludes with a set of paired propositions concerning the characteristics of “trait associations” and “personal followings.”