linear -> logistic regression

ml_table/ml_table.tex

@@ -16,7 +16,7 @@
 
 \begin{table}[h!]
     \centering
-    \caption*{\small Table 2. Machine learning algorithms comparison. Algorithms: Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Neural Network (NN), Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA), eXtreme Gradient Boosting (XGB), Generalized Linear Model (GLM), Linear Regression (LR), Cox Regression based algorithms including stepwise Cox, Coxboost, plsRcox (Cox), Supervised Principal Components (SuperPC), Elastic Network (Enet), Gradient Boosting Machine (GBM).}
+    \caption*{\small Table 2. Machine learning algorithms comparison. Algorithms: Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Neural Network (NN), Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA), eXtreme Gradient Boosting (XGB), Generalized Linear Model (GLM), Logistic Regression (LR), Cox Regression based algorithms including stepwise Cox, Coxboost, plsRcox (Cox), Supervised Principal Components (SuperPC), Elastic Network (Enet), Gradient Boosting Machine (GBM).}
     \footnotesize
     \begin{tabularx}{\textwidth}{|p{9cm}|X|X|X|X|X|X|X|X|X|X|X|X|X|X|}
     \hline

report.tex

@@ -249,7 +249,7 @@
         \hline
         \textbf{Article} & \textbf{Key results} & \textbf{Best algorithms} & \textbf{Metrics} \\
         \hline
-        A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~\cite{platinum} & Found that three immune-related proteins—CCR1, IGHV3-35, and CD72—along with the presence of postoperative residual tumors, are strong predictors of platinum resistance in EOC patients. Proposed a model that can predict the efficacy of initial platinum-based chemotherapy & Least absolute shrinkage and selection operator (LASSO) regression and linear regression (LR) & Area under curve (AUC) of 0.864 \\
+        A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~\cite{platinum} & Found that three immune-related proteins—CCR1, IGHV3-35, and CD72—along with the presence of postoperative residual tumors, are strong predictors of platinum resistance in EOC patients. Proposed a model that can predict the efficacy of initial platinum-based chemotherapy & Least absolute shrinkage and selection operator (LASSO) regression and logistic regression (LR) & Area under curve (AUC) of 0.864 \\
         \hline
         Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods~\cite{kras} & Identified some top-ranked genes, including H2AFZ, CKS1B, TUBA1B, RRM2, and BIRC5, associated with cancer progression and drug resistance. Have built efficient classifiers as the byproduct & Categorical boosting (CATB) for feature selection and support vector machine (SVM) for classification & Accuracy of 93.1\% and F1-score of 0.938 \\
         \hline