## 4 Discussion  
To our knowledge, our study is significant because it is the first to predict BM in RCC patients. Lack of studies in this area is attributed to low prevalence (only 10%) of BM in RCC patients [4], making it difficult to collect sufficient data for the application of ML in a single institution. To overcome the data shortage, we collected RCC data from multiple institutions in Korea and used SMOTE-NC to address the data imbalance problem. We developed an algorithm that uses ML to predict the probability of BM in RCC patients, compared the performance of six representative ML techniques, and concluded that AdaBoost showed optimal performance of 0.716 based on AUROC through a hyperparameter tuning process. Tree-based models, such as AdaBoost, can use the Gini index to identify the variables that have a significant effect on prediction [30]. When the effect of the AdaBoost model on the prediction of BM was examined, the most influential characteristics were found to be age, smoking, lung metastasis, pT, Heng risk group, Fuhrmann nuclear grade, ECOG PS, and gender (Figure 4), in decreasing order. According to a previous study, the risk of BM in RCC patients was higher in those younger than 70 years than in those older than 70 [13]. In our data, the mean age of the BM group was lower than that of the group without BM. When participants were divided into groups based on their age, the number of those older than 70 years was 152 (13.3%) in the group without BM and seven (5.1%) in the group with BM, showing an age distribution similar to that of previous studies. Furthermore, in previous studies, both smoking and lung metastasis, the second and third most influential variables, demonstrated that tobacco abuse and coexistence of lung metastasis were associated with a poor prognosis [7]. In our data, the proportion of current smokers was higher in the group with BM, and the number of cases of lung metastasis co-existence was higher than that in the group without BM (Table 1). Previous studies [13] have identified pT and Fuhrmann nuclear grade as important indicators for developing BM in RCC patients. In a previous study, pT was found to be a significant factor as it progressed from stage 1 to stages 3 and 4, and it was confirmed as a significant factor as the grade increased from 1 to 4. In our data distribution, 48.1% of patients without BM were at pT stage 3-4, and 31.0% had a Fuhrmann nuclear grade of 4; this compares to values of 56.9% and 39.4%, respectively, for patients with BM. Indices measured using the Heng risk group or International Metastatic RCC Database Consortium risk score show a poorer prognosis in the higher-risk group in previous studies [2]. Even in our data, the proportion of patients in the favorable category without BM was 46.2%, which was higher than that of patients with BM (36.5%), but in the high-risk category, the proportion of patients without BM was 5.2%, whereas that with BM was 8.8%. According to previous research on ECOG PS, the risk of BM is high if it is \(\geq\)1, and the risk of BM is higher in the group with a value greater than one than in the group with a value of zero [14]. In our data, the ratio of ECOG PS for the group with 0 was 79.5% without BM and 51.3% with BM. Additionally, in the case of a value greater than 1, the proportion of patients without BM was 3.2%, whereas that with BM was 6.6%. Previous studies related to sex found that women had a higher risk of BM than men. Our data also showed that the proportion of women without BM was 21.2%, but that with BM was higher at 32.1%. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **3-Fold Mean** & **5-Fold Mean** \\  & **(Standard Deviation)** & **(Standard Deviation)** \\ \hline Kernel SVM & 0.533 (0.036) & 0.504 (0.044) \\ Logistic regression & 0.640 (0.057) & 0.654 (0.076) \\ KNN & 0.525 (0.044) & 0.546 (0.024) \\ Random forest & 0.597 (0.055) & 0.599 (0.060) \\ XGBost & 0.636 (0.045) & 0.647 (0.077) \\ AdaBoost & 0.678 (0.065) & 0.696 (0.087) \\ \hline \hline \end{tabular}
\end{table}