## 4 Results
### Performance of Models  
This section contains information about the performance results of the models used in this study. The accuracy, precision, recall, F1 score, and ROC accuracy results of all models were extracted, and these results are given in detail in Table 6. Looking at the results in Table 6, the DT model gave the best result, with an accuracy rate of 92.85%. The worst result was given by the LR model, with an accuracy rate of 82.14%. Explanations of _TP_, _TN_, _FP_, and _FN_ values shown in the confusion matrix results in Table 7 are as follows:  
* _TP_ (true positive) number indicates the classification of patients with non-attack gout patients (Non-Gout) according to the test data. * _TN_ (true negative) number indicates the classification of gout attacks patients (Gout) according to the test data. * _FP_ (false positive) count indicates the number of patients in whom gout attack patients without non-attack gout patients according to the test data. * _FN_ (false negative) count indicates the number of patients in whom non-attack gout patients without gout attack patients according to the test data. The classification performance of DT, RF, LR, and ANN models used in this study in the classification of gout attack patients and non-attack gout patient \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \(\mathbf{A}\) & \(\mathbf{P}\) & \(\mathbf{S}\) & \(\mathbf{F}\) & \(\mathbf{SP}\) & **ROC AUC** \\ \hline Decision tree & 0.928571 & 0.866667 & 1.000000 & 0.928571 & 0.866667 & 0.933333 \\ Random forest & 0.857143 & 0.846154 & 0.846154 & 0.866667 & 0.856410 \\ Logistic regression & 0.821429 & 0.833333 & 0.769231 & 0.800000 & 0.933333 & 0.817949 \\ ANN & 0.857100 & 0.800000 & 0.923100 & 0.857100 & 0.800000 & 0.861500 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of ML models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **TP** & _TN_ & _FP_ & _FN_ & **Samples** \\ \hline Decision tree & 13 & 13 & 2 & 0 & 28 \\ Random forest & 11 & 13 & 2 & 2 & 28 \\ Logistic regression & 10 & 13 & 2 & 3 & 28 \\ ANN & 12 & 12 & 3 & 1 & 28 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Detailed CM results of the models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Models** & \\ \hline
**Class** & **DT** & **RF** & **LR** & **ANN** \\ \hline Gout & 13 & 11 & 10 & 12 \\ Non-gout & 13 & 13 & 13 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Disease classification performance of the models. Along with the results of the classification performance of the models, the features affecting the classification performance of the model were also determined. The purpose of determining these features is to determine the features that affect the performance of the models and to reveal the conditions that are effective in gout attack with ML models. Figure 2 shows the ROC curve of the RF model. Table 9 shows the most important factors affecting classification performance with the random forest (RF) model and determining gout disease status. Accordingly, the most important factor determining the gout disease status is whether the patient has Creatin or not with 23.41%. When Table 10 is examined, the decision tree (DT) model shows the most important factors that determine gout disease status. According to this, the most important factor  
Figure 2: ROC curve of RF. \begin{table}
\begin{tabular}{c c} \hline
**Feature Name** & **RF Classification (\%)** \\ \hline CREATIN & 0.234179 \\ UREA & 0.162304 \\ HAEMOGLOBIN & 0.093477 \\ ALBUMIN & 0.043521 \\ URIC ACID & 0.042693 \\ LDH & 0.031623 \\ GLUCOSE & 0.027593 \\ MCV & 0.026070 \\ ALT & 0.025176 \\ LYMPHOCYTE & 0.024879 \\ EOSINOPHIL & 0.023152 \\ PLT & 0.022635 \\ HDL & 0.022486 \\ TOTAL PROTEIN & 0.021319 \\ SEDIMENTATION & 0.020957 \\ MONOCYTE & 0.020675 \\ WBC & 0.020099 \\ CRP & 0.019134 \\ LDL & 0.018854 \\ MPV & 0.017552 \\ \hline \end{tabular}
\end{table} 
Table 9: The most important factors identified with RF.