## 4 Results
### Classification
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Word-Embedding**} & \multirow{2}{*}{**LR [\%]**} & \multicolumn{2}{c}{**F1-Macro**} & \multirow{2}{*}{**RF [\%]**} & \multirow{2}{*}{**k-NN [\%]**} \\  & & & **MLP [\%]** & & **SVM [\%]** & & \\ \hline \multirow{4}{*}{Average} & FastText & \(65.71\pm 0.93\) & \(65.72\pm 1.33\) & \(66.36\pm 0.94\) & \(50.57\pm 0.78\) & \(50.91\pm 1.28\) \\  & GloVe No Correction & \(66.15\pm 0.67\) & \(66.04\pm 1.24\) & \(67.42\pm 1.41\) & \(51.80\pm 1.02\) & \(57.13\pm 1.44\) \\  & W2V No Correction & \(64.27\pm 0.93\) & \(66.08\pm 0.89\) & \(66.60\pm 0.99\) & \(50.69\pm 0.60\) & \(56.37\pm 0.63\) \\  & Gloe Correction & \(68.12\pm 1.38\) & \(69.13\pm 0.77\) & \(69.46\pm 0.87\) & \(54.01\pm 0.69\) & \(59.69\pm 1.05\) \\  & W2V Correction & \(62.70\pm 1.97\) & \(67.43\pm 0.89\) & \(67.39\pm 0.72\) & \(51.55\pm 0.70\) & \(57.29\pm 1.49\) \\  & W2V Trained & \(24.69\pm 0.54\) & \(51.49\pm 0.82\) & \(65.91\pm 0.82\) & \(51.23\pm 0.67\) & \(54.81\pm 1.08\) \\ \hline \multirow{4}{*}{Clustering} & FastText & \(64.59\pm 0.82\) & \(69.37\pm 1.32\) & \(65.69\pm 1.65\) & \(60.46\pm 2.08\) & \(54.86\pm 1.50\) \\  & GloVe No Correction & \(62.81\pm 0.68\) & \(67.11\pm 1.05\) & \(63.99\pm 0.48\) & \(59.21\pm 1.59\) & \(51.71\pm 1.78\) \\  & W2V No Correction & \(63.66\pm 1.03\) & \(68.48\pm 0.87\) & \(65.49\pm 0.90\) & \(62.43\pm 1.54\) & \(52.10\pm 1.08\) \\  & GloVe Correction & \(66.59\pm 0.95\) & \(70.36\pm 0.97\) & \(67.73\pm 1.69\) & \(62.44\pm 1.66\) & \(54.78\pm 1.80\) \\  & W2V Correction & \(66.55\pm 0.80\) & \(70.54\pm 1.92\) & \(67.14\pm 0.99\) & \(62.31\pm 1.42\) & \(55.67\pm 2.05\) \\  & W2V Trained & \(64.19\pm 1.30\) & \(68.52\pm 0.63\) & \(65.74\pm 0.70\) & \(59.63\pm 0.62\) & \(54.65\pm 0.32\) \\ \hline \multirow{4}{*}{PCA} & FastText & \(47.64\pm 0.53\) & \(52.30\pm 1.26\) & \(50.44\pm 2.32\) & \(37.96\pm 1.03\) & \(34.11\pm 1.59\) \\  & GloVe No Correction & \(55.86\pm 1.12\) & \(61.83\pm 2.45\) & \(63.18\pm 1.87\) & \(48.07\pm 0.95\) & \(50.00\pm 1.51\) \\ \cline{1-1}  & W2V No Correction & \(52.44\pm 1.73\) & \(61.25\pm 1.63\) & \(63.25\pm 1.13\) & \(50.17\pm 1.08\) & \(52.56\pm 1.29\) \\ \cline{1-1}  & GloVe Correction & \(55.71\pm 1.40\) & \(61.70\pm 2.23\) & \(63.32\pm 1.62\) & \(49.80\pm 1.41\) & \(53.11\pm 1.41\) \\ \cline{1-1}  & W2V Correction & \(64.26\pm 1.03\) & \(66.88\pm 0.76\) & \(67.54\pm 0.93\) & \(55.63\pm 0.97\) & \(59.89\pm 2.56\) \\ \cline{1-1}  & W2V Trained & \(38.62\pm 0.62\) & \(52.88\pm 2.31\) & \(53.31\pm 1.36\) & \(45.79\pm 0.42\) & \(48.08\pm 0.67\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean and standard deviation for different models in k-fold cross-validationIn addition, thetwo confusion matrices with the best performances on the algorithm of PCA and average representation of the document in the test set are shown in Figure 2. These confusion matrices provide a breakdown of the actual groups, which are listed vertically, and the predicted groups, which are listed horizontally. The normalized outcome of the correct prediction is listed diagonally. We noticed the best performances in the groups of MC and HD, and the worst performances were found in the categories with the least number of documents, such as NS and PI. In addition, most of miss-classification occurs in the groups of OC and WS. To offer a more comprehensive assessment of the model's clinical utility [36], Table 5 presents the calculated positive predictive values (PPVs) and true positive rates (TPRs) for the best-performing models on the test set. Our emphasis on these metrics is due to the model's role as a potential screening solution. We have chosen not to report metrics dependent on true negative values. This decision is based on the substantial sample imbalance,