Logistic regression analysis estimated relative risk, 95% confidence intervals, and P-values.#Age and the independent predictors of a positive biopsy result (elevated PSA, decreased free to total PSA ratio, small prostate volume, and abnormal digital rectal examination findings) were used to develop...
Furthermore, a logistic regression analysis was performed to determine the independent risk factors for colorectal adenoma in these subjects. Results: Of the 581 participants, 55 presented with gallbladder polyps and 526 did not have gallbladder polyps. Participants with gallbladder polyps showed a ...
Table 1 presents the classification results of directly applying models trained on the source domain to the target domain. We utilized seven classic classification models: support vector machine (SVM)2, logistic regression (LR)49, decision tree (DT)50, k-nearest neighbors (KNN)51, random forest...
Pattern classification analyses were performed using penalized (L2) logistic regression implemented via the sklearn module (0.24.2) in Python and custom Python code. For all classification analyses, classifier features were comprised of spectral power across 63 electrodes and 46 frequencies. Before patte...
Univariate and multivariate logistic regression analyses were performed. Results: In the univariate analysis, age ≥75 years (odds ratio [OR], 2.4; 95% confidence interval [CI], 1.3 to 4.2), H. pylori-positivity (OR, 2.0; 95% CI, 1.2 to 3.5), and the concomitant use of proton pump ...
Logistic regression can solve non-linear problems by modifying the decision boundary, which is the hypothetical function (ax + b), to a non-linear feature (Denoeux, Citation2019). 2.2. Clustering Clustering techniques group the data points together based on similarity. In general, there are...
We have realized a univariate (Chi2 test) and multivariate (logistic regression test) statistic analysis concerning 7 sub-groups defined according to the literature on the TVT. Age > 55 years ( P = 0,044) and SUI grade > 2 ( P = 0,028) are statistically associated with a decrease of...
In this study, we employed and compared seven widely applied classifiers and strategies, including logistic regression (LR)43, support vector machine (SVM)44 with radial basis function (RBF) kernel, stochastic gradient descent (SGD)45, AdaBoost46 based on decision tree (DT)47, RF25, XGBoost24...
A multivariate logistic regression analysis was performed to determine the risk factors independently associated with poor pathologic response in the patients included in the study, and the results are presented in Table 5. Poor pathological response was found to be associated with younger ages (P=0.0...
Multivariate logistic regression analysis showed a strong relationship between family history of type 2 diabetes with both abdominal obesity (odds ratio [OR] 4.2, CI 95% 1.9-10.1, p <0.05) and fasting hyperinsulinemia (OR 3.1, CI 95% 1.4-11.2, p <0.05).Conclusions In the absence of ...