## 3 Results  
Figure 2a,b depict a clear difference on the Z-axis variables of the critical MFC heights. MFC height in the range of the critical threshold based on the young population's lower end of the normal range (i.e., mean\(-\)SD) indicated the Z-axis component of the kinematic variable is highly unstable with increased tripping risk. Figure 3 is the correlation plot that helped us visualize the relationship between the variables and the target. This study followed best practices and employed a range of classical performance metrics for classification, including confusion matrix, classification accuracy, precision, recall, F1-score, and the ROC AUC [64]. A combination of the metrics was used where high accuracy referred to the model's high level of overall correctness, with high recall showing the proportion of actual positive cases and precision indicating the proportion of positive identifications made by the model that were actually correct. The F1-score is used to show the balance of precision and recall, and the ROC AUC score shows the ability of the model to discriminate between classes. Figure 4a-c represent the classification results derived from the confusion matrix for KNN, Random Forest, and XGBoost with close-matched accuracies in KNN and Random Forest. Higher accuracies are prioritized in ML modeling, while precision and recall rate gave further insight on the overall performance of the model. For example, high recall signifies good coverage, i.e., the percentage of positive tags the classifier predicted correctly out of all the positive tags available in the sample, while precision is indicative of the proportion of actually positive classes out of all the classes predicted as positive [65]. The F1-score measures recall and precision at the same time using harmonic means. It implies the model's balanced ability to both capture true positive cases (recall) and be accurate with the cases it does capture (precision). Both precision and recall are represented in the three categories supported by the F1-scores. In the KNN model, a particular MFC height is predictable with 84 percent accuracy. Recall and precision are 92 and 96 percent, respectively, on R3 (when MFC heights are above safety limits). At the same time, the F1-score on R3 is 94 percent but decreased in the R1 and R2 categories. Similarly, in the Random Forest model, recall, precision, and F1-scores have the same value at above safety regions (R3) with an overall model performance of 86 percent. Random Forest was more sensitive than KNN and XGBoost at lower MFC height categories. At best, XGBoost has an average performance of 75 percent throughout. Like KNN and Random Forest, XGBoost optimal prediction was above safety limits (R3) and performed the least at below safety (R1) and safety categories (R2). XGBoost had a nearly 50-50 chance of predicting positive cases of safety MFC heights with an F1-score of 56 percent and 10 percent higher when the MFC height is below average. These are regions which measure very low MFC heights. The model training time for KNN was much quicker at 0.39 s compared to 13.98 s with Random Forest and 170.98 s with XGBoost, as shown in Table 3.  
The bar chart shows that the KNN parameters developed in this model are good at predicting positive cases of MFC height that are well above the safety limit. It performed better in accurately classifying MFC heights at the safety and well-above safety limits than when MFC height is below average. There is a near-equitable distribution of the metric with an overall accuracy of 86%. Recall and precision of MFC heights below average (R1) are lower than observed at safety and well-above safety MFC heights. Precision, recall, and F1-score of Random Forest and KNN have similar performance indexes in well-above safety regions. In summary, the performance result with Random Forest is similar to the KNN algorithm, with a slight improvement in overall accuracy and recall and a higher training time.