## 5 Conclusions  
In this study, we investigated the application of machine learning (ML) techniques to identify key factors influencing the progression of gouty arthritis (GA) and its impact on renal function. By leveraging demographic, clinical, and laboratory data, we utilized decision tree (DT), random forest (RF), logistic regression (LR), and artificial neural network (ANN) models to classify GA and determine the most significant biomarkers associated with disease severity. The evaluated ML models showed that they were able to effectively classify GA cases based on glomerular filtration rate (GFR) values, and showed effective performances in identifying important biomarkers affecting GA. Among the models, the decision tree (DT) model outperformed the others, achieving the highest accuracy (92.85%) and recall (100%), \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Year** & **Dataset** & **Model** & **Metrics** & **Results** \\ \hline \multirow{3}{*}{[28]} & \multirow{3}{*}{2023} & 2010–2019 collected dataset of & \multicolumn{2}{c}{Multivariable} & \multirow{3}{*}{R\({}^{2}\)} & \multirow{3}{*}{0.74} \\  & & & linear & & \\  & & 4.637 patients with type 2 diabetes & mixed-effects & & \\  & & & model & & \\ \cline{1-1}  & & 22991 observation data for & \multicolumn{2}{c}{dLGFR} & Accuracy & 0.8830 \\ \cline{1-1}  & & 5186 patients & \multicolumn{2}{c}{dLGFR} & \multicolumn{1}{c}{MSE} & 294.24 \\ \cline{1-1}  & & 2024 & \multicolumn{2}{c}{13 different cohorts of 19.629 patients} & \multicolumn{1}{c}{RF} & \multicolumn{1}{c}{MSE} & 294.24 \\ \cline{1-1}  & & 2025 & \multicolumn{2}{c}{Data on 276 patients between 2018 and 2023} & \multicolumn{1}{c}{DT} & \multicolumn{1}{c}{Accuracy} & 0.9285 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of study findings with the literature. Figure 7: Loss graphics of ANN. highlighting its effectiveness in detecting GA. In contrast, the random forest (RF) model showed a more balanced performance with an accuracy of 85.71%, indicating that it can generalize better to larger datasets. Logistic regression stood out with the highest specificity (93.33%), effectively reducing false positive classifications. On the other hand, the artificial neural network (ANN) model exhibited strong recall (92.31%) but the lowest precision (80.00%), indicating a tendency to misclassify cases without gouty arthritis as positive. Among the most influential factors, creatinine, urea, hemoglobin, and albumin levels were identified as key indicators of GA progression. These findings highlight the potential of ML-based predictive models in improving early diagnosis, risk assessment, and personalized treatment strategies for GA patients. Despite the promising results, this study has certain limitations. The dataset size was relatively small, which may impact on the generalizability of the findings. Future research should focus on expanding the dataset to include a larger and more diverse patient population. Additionally, integrating more advanced deep learning techniques and longitudinal patient data could enhance predictive accuracy and provide deeper insights into disease progression. In conclusion, this study underscores the potential of ML-driven approaches in medical research, particularly in the assessment of GA and renal function impairment. By incorporating data-driven methodologies, clinicians can refine diagnostic processes, improve patient outcomes, and develop more effective treatment strategies for individuals at risk of GA-related complications. Conceptualization, O.C. and F.B. ; data collection, O.C. ; design of models, F.B. ; literature research, O.C. and F.B. ; writing--review and editing, O.C. and F.B. ; writing--original draft preparation, O.C. and F.B. All authors have read and agreed to the published version of the manuscript. This research was funded by Recep Tayyip Erdogan University Development Foundation, grant number 02024009018070.