## 3 Results
Random Forest randomly selects a subset of features that are used as candidates at each split. This protocol automatically prevents the multitude of decision trees from relying on the same set of features, solving problems of overestimated correlations by avoiding a correlation of the individual trees. Each tree then draws a random sample of data from the training dataset when generating its splits, which further introduces an element of randomness and prevents the individual trees from overfitting the data. The uniformlygenerated weighted average on R1, R2, and R3 with both algorithms showed that each class was equally considered in its calculation of the metrics and had equal impact on the average score for each of those metrics (Table 3). Figure 4: _Cont_. Table 3 shows the comparison between prediction accuracy and model training time on KNN, Random Forest, and XGBoost. XGBoost has the lowest accuracy and highest training. KNN has the quickest training time and closely follows Random Forest in accuracy. A comparison of the ROC AUC scores is presented in Figure 4d, where all three algorithms show high discrimination capacity, with KNN outperforming the rest (consistent with the prior results). Further assessment was performed on feature importance as presented in Figure 4e. The results of the algorithm showed that the three most important features for the classification were measurements from angular velocity Z (Gyro_Z) and linear acceleration Z (Acc_Z). This confirmed the initial results that the increased tripping risk is associated with the Z-axis components. We further examined the prediction accuracies of the separate linear accelerations and angular velocities together and as individual features contributing to the MFC heights. The results are shown in Table 4a.b. The feature importance assessment was conducted using the'sklearn' package and is shown graphically. It was observed that the combined linear acceleration and angular velocity features had a more positive impact on the overall performance accuracies of the three models. However, individual features contributing to the prediction varied. Table 4b showed that the Z-axis component of the linear acceleration and the X-axis and Y-axis components of the angular velocity had greater influence on the MFC heights than the other kinematic features used for the training. Figure 4: (**a**) Performance evaluation of the KNN algorithm showing average accuracy of 84 percent with high recall and F1-scores. (**b**) Performance evaluation of Random Forest with average accuracy of 86 percent with high recall and F1-scores. (**c**) Performance evaluation of XGBoost stands at 75% accuracy in classifying MFC heights into the following three pre-defined categories: below average, safety, and well-above safety limits. The recall, precision, and F1-score index at well-above safety limits are below that observed with Random Forest and KNN. XGBoost can detect positive cases at well-above safety limits with a greater chance of error than observed in other models. (**d**) Comparison of model performances based on ROC AUC where higher values indicate a better separability, which is higher discrimination capacity to distinguish between classes and further comparison for discrimination capacity between each class. (**e**) Feature importance evaluation in classing MFC heights into the following three pre-defined categories: below average, safety, and well-above safety limits. Table 4a shows the angular velocity had a more positive effect on the predicted MFC heights than the linear acceleration, and MFC height is best predicted with multiple features. Table 4b indicated that the vertical acceleration and the X and Z axes of the angular velocities are more significantly related to the MFC height than the other kinematic variables (_p_ < 0.001). The ROC AUC scores show how well the model performs with no knowledge of the class imbalance and take values from 0-1. Table 5 shows the AUC scores on all three models in each target class.