# Using Machine Learning to Shorten and Adapt Fall Risk Assessments for Older Adults
## 4 Results
### Results Part C  
The ML-CAT algorithm was trained and tested only on feasible tasks (Section 3.5) using Dataset 2, which allowed for a larger set of feasible features than Dataset 1 (Results Part B). In addition to predicting the MBT, three additional models were trained and tested to predict the non-feasible tasks, namely, the TUG, 4 SBT, and 4 MW, by using only feasible tasks. The models are denoted as follows: Model C_MBT, Model C_TUG, Model C_4 SBT, and Model C_4 MW. The last three use multiple tasks to predict a single task; however, considering their significance in assessing balance [34; 35], exploring alternative methods to predict their scores is of importance.  
The feasible task scores were used to train an RF for each model, and feature ranking was used to determine the most predictive feature. Table 4 shows the top informative tasks derived from feature ranking on each model. The most informative task for each model was chosen as the initial task.  
Figure 10 shows the average percentage error of the predicted scores obtained as a function of the number of tasks administered using the ML-CAT sequencing. Each model was trained and tested independently, though the results are displayed in a single plot. For all models, the average percentage error decreased as a function of the number of tasks.  
Table 5 shows the results for different levels of confidence when following Stopping Criterion 2. It can be seen that higher levels of confidence achieve better accuracy (a smaller percentage error) but require a larger number of tasks. Detailed results for each model follow.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Top Ranking Features** & **Predictive Factor** \\ \hline \multirow{3}{*}{Model C\_MBT} & Sit-to-stand test (STS) & 23 \\  & Fourth stage (4 SBT) & 21 \\  & Stance eyes closed (MBT) & 16 \\ \hline \multirow{3}{*}{Model C\_TUG} & Sit-to-stand test (STS) & 41 \\  & Fourth stage (4 SBT) & 23 \\  & Standing on one leg (BBS) & 6 \\ \hline \multirow{3}{*}{Model C\_4 SBT} & Standing on one leg (BBS) & 86 \\  & Sit-to-stand test (STS) & 6 \\  & Stand on one leg (MBT) & 3 \\ \hline \multirow{3}{*}{Model C\_4 MW} & Sit-to-stand test (STS) & 44 \\  & Fourth stage (4 SBT) & 21 \\  & Turn to look behind (BBS) & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The top three informative tasks for each model and their predictive score (Model C).  
Figure 10: Average percentage error calculated for each Model C.  
**Model C_MBT:** The sit-to-stand test is the initial task for this model (see Table 4). Using Stopping Criterion 1 and terminating the ML-CAT algorithm after six steps, an average error of 1.28 MBT points (16%) is obtained (Figure 10). Using Stopping Criterion 2 and setting the confidence level to 87.5%, an average percentage error of 5.61% is obtained, requiring an average of six tasks (Table 5).  
**Model C_TUG:** The sit-to-stand test is the initial task for this model (Table 4). Using Stopping Criterion 1 and terminating the algorithm after three steps, an average error of 1.1 s (12%) is obtained (Figure 10). Using Stopping Criterion 2 and setting the confidence level to 90%, an average percentage error of 12.81% is obtained, and about three tasks are required (Table 5).  
**Model C_4 SBT:** Stand on one leg is the initial task (Table 4). Using Criterion 1 and terminating the algorithm after three tasks, an average error of 2 s (10%) is obtained (Figure 10). Using Stopping Criterion 2 and setting the confidence level to 93%, an average percentage error of 9.8% is obtained, and, on average, five tasks are required (Table 5).  
**Model C_4 MW:** The sit-to-stand test is again chosen as the initial task (Table 4). Using Criterion 1 and terminating the algorithm after three tasks, an average error of 0.36 m/s (14.2%) is obtained (Figure 10). The prediction does not improve with additional tasks. Using Stopping Criterion 2 with a confidence level of 87%, an average percentage error of 11.32% is obtained, and about four tasks are required (Table 5).