## 5 Experimental Study
### Modelling Results
#### 5.2.1 Rheumatoid Arthritis Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Method** & **Accuracy on the Training Set** &
\begin{tabular}{c} **Accuracy for** \\ **Cross-Validation** \\ \end{tabular} & **Accuracy for Leave-One-Out** \\ \hline Random forest, & 100\% & 57.8313\% & 62.6506\% \\
100 trees & 100\% & 65.0602\% & 65.0602\% \\ \hline NN & 100\% & 63.8554\% & 67.4699\% \\ \hline kNN, & 100\% & 72.2892\% & 72.2892\% \\ k = 20, w = 1/d & **100\%** & **84.6988\%** & **74.6988\%** \\ \hline C4.5, & 74.6988\% & 69.8795\% & 72.2892\% \\ Pruned & 85.5422\% & 61.4458\% & 67.4699\% \\ \hline SVM, & 89.1566\% & 69.8795\% & 67.4699\% \\ Puk kernel & 89.1566\% & 69.8795\% & 67.4699\% \\ \hline \hline \end{tabular}  
* For this dataset, additional experiments were performed to assess the performance of the algorithms from the point of view of precision and recall. Since the dataset was unbalanced (62 instances in class 0 and 21 instances in class 1), such analyses can provide additional information beyond the assessment of accuracy. For the best representative of each class of machine learning methods, the following indicators were computed: the true positive (TP) rate, the false positive (FP) rate, precision, recall, and the F1 score. In each cell, the first value is for class 0, the second one is for class 1, and the third one is the weighted average of the first two values.  
\begin{tabular}{c c c c c} \hline \hline
**Classification Method** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F1** \\ \hline Random forest, & 0.839 & 0.905 & 0.732 & 0.839 & 0.782 \\
1000 trees & 0.095 & 0.161 & 0.167 & 0.095 & 0.121 \\
0.651 & 0.717 & 0.589 & 0.651 & 0.615 \\ \hline kNN, & 1 & 1 & 0.747 & 1 & 0.855 \\ k = 83 (all), w = 1/d & 0 & 0 & N/A & 0 & N/A \\
0.747 & 0.747 & N/A & 0.747 & N/A \\ \hline C4.5, & 0.935 & 1.000 & 0.734 & 0.935 & 0.823 \\ Pruned & 0 & 0.065 & 0 & 0 & 0 \\
0.699 & 0.763 & 0.548 & 0.699 & 0.615 \\ \hline SVM, & 0.935 & 1 & 0.734 & 0.935 & 0.823 \\ Puk kernel & 0 & 0.065 & 0 & 0 & 0 \\
0.699 & 0.763 & 0.548 & 0.699 & 0.615 \\ \hline \hline \end{tabular}  
\end{table} 
Table 9: Precision and recall analysis for the original dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Method** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F1** \\ \hline Random forest, & 0.839 & 0.905 & 0.732 & 0.839 & 0.782 \\
1000 trees & 0.095 & 0.161 & 0.167 & 0.095 & 0.121 \\
0.651 & 0.717 & 0.589 & 0.651 & 0.615 \\ \hline kNN, & 1 & 1 & 0.747 & 1 & 0.855 \\ k = 83 (all), w = 1/d & 0 & 0 & N/A & 0 & N/A \\
0.747 & 0.747 & N/A & 0.747 & N/A \\ \hline C4.5, & 0.935 & 1.000 & 0.734 & 0.935 & 0.823 \\ C4.5, & 0 & 0.065 & 0 & 0 & 0 \\ Pruned & 0.699 & 0.763 & 0.548 & 0.699 & 0.615 \\ \hline SVM, & 0.935 & 1 & 0.734 & 0.935 & 0.823 \\ Puk kernel & 0 & 0.065 & 0 & 0 & 0 \\
0.699 & 0.763 & 0.548 & 0.699 & 0.615 \\ \hline \hline \end{tabular}  
\end{table} 
Table 8: Classification results when only the latest record for each patient is retained. Moreover, the C4.5 algorithm was applied again to determine a decision tree (Table 12). \begin{table}
\begin{tabular}{|c c c c c c|} \hline \hline
**Classification Method** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F1** \\ \hline Random forest, & 0.645 & 0 & 1 & 0.645 & 0.784 \\
1000 trees & 1 & 0.355 & 0.741 & 1 & 0.851 \\
0.824 & 0.179 & 0.87 & 0.824 & 0.818 \\ \hline kNN, & 0.435 & 0 & 1 & 0.435 & 0.607 \\ kN = 124 (all), w = 1/d & 1 & 0.565 & 0.643 & 1 & 0.783 \\
0.72 & 0.285 & 0.82 & 0.72 & 0.695 \\ \hline C4.5, & 0.532 & 0.127 & 0.805 & 0.532 & 0.641 \\ Pruned & 0.873 & 0.468 & 0.655 & 0.873 & 0.748 \\
0.704 & 0.299 & 0.729 & 0.704 & 0.695 \\ \hline SVM, & 0.694 & 0.127 & 0.843 & 0.694 & 0.761 \\ Puk kernel & 0.873 & 0.306 & 0.743 & 0.873 & 1 \\
0.784 & 0 & 1 &  
These tables present explicit decision rules or trees.