## 4 Results of Dataset and Classification
### Results of Classification  
Figure 7 shows the results of training through AutoML. The current AutoML trains and cross-validates many kinds of algorithms: XGBoost GBM (Gradient Boosting Machine), GLMs, default Random Forest (DRF), H2O GBMs, Deep Neural Net, Randomized Forest (XRT), and XGBoost GBMs. Thus, there is no need to consider which algorithms are proper for the prepared dataset because AutoML automatically looks into the results of all algorithms.  
Figure 5: An example of constructed dataset for this study.  
Figure 6: Results of data distribution under the condition of three different walking speeds: slow walking, preferred condition, and fast walking.  
The training results found that it was possible to classify the difference in gait and physical fitness characteristics between senior citizens with or without diabetes with 80% accuracy through the non-invasive method.  
Figure 8 shows the results of the confusion matrix for the prediction through DRF, which is the highest accuracy in used algorithms. Among the total data, 165 were used for training (80%), and 35 were used for prediction (20%). It was confirmed that the error rate was so low. That means that the applied algorithm was suitable for classifying the difference between senior citizens with and without diabetes.  
Figure 9 shows the results of the variable importance plot, which show the relative importance of the essential variables in the model. Variable importance is currently available for all H2O models; so, if you happen to use h2o.explain() [34] on an AutoML object with a Stacked Ensemble at the top of the leaderboard, it instead shows the variable importance for the top \"base model\", which is DRF for this study, as in Figure 7.  
Figure 8: Results of confusion matrix for the prediction through the default random forest (DRF), which is the highest accuracy in used algorithms.  
Figure 7: Results of training through automated machine learning (AutoML). “auc” is related to the accuracy results of classification produced by the trained model, and “logloss” is that the cross-entropy between the model and the target values. “rmse” is the root-mean-square error metric, and “mse” is the mean square error. “mean_per_class_error” is one kind of available options for classification.  
As a result, it was found that the variable of single_leg_balance_s was the most dominant. That meant that the balance stability might decrease more with senior citizens with diabetes than those without diabetes. Then, we found that senior citizens exhibited more insufficient gait stability at slower and faster strides. Thus, the different walking speeds must help evaluate gait characteristics to distinguish senior citizens with diabetes and controls. Additional important variables in the model were fast_CV_stance_phase_percent, standing_time_from_a_long_sitting_position_s, six_min_walk_s, slow_CV_stride_length_percent, fast_CV_stride_time_percent, three_meter_TUG_s, and non-physical performance variables, such as MMSE_score, hypertension, and total_physical_activity/MET_min_week. Variability (CV) domain of gait exhibited to be an important factor in senior citizens with diabetes.