Сравнение используемых алгоритмов

report.tex

@@ -189,6 +189,28 @@
         \end{tabularx}
     \end{table}
     
+    \newpage
+    \begin{table}[h!]
+        \centering
+        \caption{Machine learning algorithms comparision.}
+        \footnotesize
+        \begin{tabularx}{\textwidth}{|p{3cm}|c|c|c|c|c|c|c|X|c|c|}
+        \hline
+        \textbf{Article} & \textbf{DT} & \textbf{NB} & \textbf{KNN} & \textbf{SVM} & \textbf{NN} & \textbf{LASSO} & \textbf{RF} & \textbf{PCA-LDA} & \textbf{XGB} & \textbf{GLM} \\
+        \hline
+        Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning~\cite{paclitaxel} & + & + & + & + & + &  &  &  &  & \\
+        \hline
+        Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer~\cite{heterogeneity} &  &  &  &  &  & + &  &  &  & \\
+        \hline
+        Mitochondria-related chemoradiotherapy resistance genes-based machine learning model associated with immune cell infiltration on the prognosis of esophageal cancer and its value in pan-cancer~\cite{mitochondria} &  &  & + & + & + & + & + &  & + & + \\
+        \hline
+        Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~\cite{sers} &  &  &  &  &  &  &  & + &  & \\
+        \hline
+        \end{tabularx}
+    \end{table}
+
+    %  & \textbf{PCA-LDA}
+
     \newpage
 
     \begin{table}[h!]