## 5 Results  
This study made predictions on the test dataset using a model based on the entire training data. The BMI values predicted by the model were analyzed using SHAP. Figure 2 shows the feature importance in XGBoost model using SHAP, which indicates that height is a highly importance feature. However, according to the BMI calculation formula, height already had a high correlation with BMI, regardless of model performance. It may not be possible to measure the influence of other variables correctly. Therefore, a clustering method was used to divide the data according to height, and the relationships between the variables and BMI were examined in each height group. The reason for dividing the groups based on height was to accurately identify the degree of influence of the changeable activity variable on the BMI of users in the same group, as height is an immutable variable. According to the National Statistical Office of Korea and previous studies, the average heights of women in their 30s, 40s, and 50s in Korea are 161.59, 159.91, and 157.23 cm, respectively; the average heights of men in their 30s, 40s, and 50s in Korea are 174.05, 172.15, and 169.39 cm, respectively [41; 42]. Figure 3 also shows that height was an important feature. Based on height, we divided the women's data into two groups, 150-160 cm and 160-170 cm, and the men's data into three groups, 165-170 cm, 170-175 cm, and 175-180 cm. To reduce the imbalance in the data due to excessive grouping, we divided the men's data by 5 cm. For females, we formed two groups because the amount of data in a given group became too small when divided by 5 cm, resulting in performance problems. Figure 3: Importance of variables calculated using SHapley Additive exPlanations. Tables 2 and 3 summarize the performance indicators of the three models in each cluster for men and women, respectively. For modeling women's data, it was decided to use only XGBoost based on the men's data analysis results. The XGBoost algorithm is generally similar to or superior to Ridge Regression and CatBoost algorithms. The benefits of using CatBoost are limited because the features used for analysis have few categorical features. The main relevant variables for women's group 1 were calories burned, distance, number of steps, time walked at night, and total sleep time per day (Figure 4). A figure illustrating the data of women's group 2 can be found in the Appendix A (Figure A1). The main variables of women's group 2 were calories burned by walking, total sleep time per day, total sleep time variability, gait variability, and average walking speed at night. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Men** & **Model** & **Explained Variance Score** & **R Squared** & **Adjusted R Squared** & **MAE** & **RMSE** \\ \hline \multirow{3}{*}{Group 1} & XGBoost & 0.600 & 0.589 & 0.568 & 1.173 & 1.569 \\ \cline{2-7}  & CatBoost & 0.611 & 0.605 & 0.574 & 1.240 & 1.641 \\ \cline{2-7}  & Ridge & \(-\)0.040 & \(-\)20.487 & \(-\)22.171 & 11.807 & 12.104 \\ \hline \multirow{3}{*}{Group 2} & XGBoost & **0.570** & **0.568** & **0.560** & **1.131** & **1.455** \\ \cline{2-7}  & CatBoost & 0.319 & 0.316 & 0.295 & 1.305 & 1.850 \\ \cline{2-7}  & Ridge & \(-\)0.013 & \(-\)1.434 & \(-\)1.510 & 2.871 & 3.490 \\ \hline \multirow{3}{*}{Group 3} & XGBoost & **0.546** & **0.546** & **0.533** & **1.336** & **1.943** \\ \cline{2-7}  & CatBoost & 0.538 & 0.533 & 0.532 & 1.346 & 2.076 \\ \cline{2-7}  & Ridge & 0.316 & \(-\)107.124 & \(-\)134.390 & 38.207 & 38.328 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the Performance Indicators of the XGBoost, CatBoost, and Ridge Regression Machine Learning Algorithms for each height group. Figure 4: Feature importance calculated using the SHapley Additive exPlanations for women’s group 1. The men's data were analyzed in the same way. The main variables of men's group 1 were average morning walking speed, calories burned, total sleep time variability, daily average walking speed, and daily total sleep time.