# Using Machine Learning to Shorten and Adapt Fall Risk Assessments for Older Adults
## 4 Results
### Results Part A  
The ML-CAT model was trained to predict the BBS final score (Model A-BBS) and the MBT final score (Model A-MBT) using Datasets 1 and 2, respectively. The aim of Model A-BBS was to enhance the performance of the model described in [22], where the fall risk category (low, middle, or high) was predicted. Model A-BBS predicts the final numeric BBS score rather than a categorical score. Model A-BBS used Dataset 1 and followed the algorithm given in Section 3 and Figure 1. The 14 BBS task scores served as features, and the physiotherapist's final score for the BBS test served as the ground truth label to train and test the model. The first task to be administered was determined by training an RF with all features and performing feature ranking [32]. Figure 2 shows the resulting ranking. The \"turn 360 degrees\" task was the most informative; thus, it was chosen as the initial task. As described above, LOO was used for testing. Figure 3 shows the average prediction error obtained as a function of the number of tasks administered using the ML-CAT sequencing. It can be seen that the MAE and the average percentage error decreased as a function of the number of tasks. Using Stopping Criterion 1 and terminating the algorithm after four steps, an average error of 2 BBS score points (4% error) was obtained. Using Stopping Criterion 2, the average prediction error decreased with the confidence level at the cost of an increased average number of tasks. This can be seen in Table 2, which shows the results for different levels of confidence, as evaluated on the test set. For each confidence level, the average number of tasks required to reach that level is reported, as well as the resulting average percentage error. Using Stopping Criterion 2 and setting the confidence level to 96%, an average error of 3 BBS score points (6.02% percent error) was obtained while requiring an average of five tasks to be performed per subject. For each subject, ML-CAT derived a different sequence of BBS tasks. Figure 4 shows a frequency table depicting the number of occurrences of tasks at each positional order in the adaptive sequences when ML-CAT was allowed to run the full 14 cycles. The columns indicate the position in the sequence. Thus, for example, 68 subjects had \"standing on one leg\" as their second task, and 78 subjects had \"standing unsupported one foot in front\" as their third task. The adaptivity of the algorithm to each individual can be seen in the spread of tasks as the sequence progresses. Figure 2: Feature importance derived from the RF model trained on all 14 BBS tasks to predict the BBS score (Model A-BBS). Using ML-CAT, performance improved over that in [22], where an accuracy of 96-97% was obtained using, on average, 4-6 BBS tasks to predict the categorical level of risk of fall. We showed that ML-CAT requires on average 4-6 BBS tasks to achieve a similar 3-4% error; however, the prediction consisted of the exact numeric final score of the BBS test rather than a categorical prediction, as in [22]. The same modeling and analysis described for the BBS (Model A-BBS) were applied to predict the final numeric MBT score using Dataset 2 (Model A-MBT). The 14 MBT task scores served as features, and the physiotherapist's final score for the MBT test served as the ground truth label. The \"stepping correction lateral\" task was chosen as the initial task for this model based on ranking (Figure 5). As described above, the LOO approach was used on the N = 155 subjects in Dataset 2. Figure 6 shows the resulting average prediction error. Using Stopping Criterion 1 and terminating the algorithm after four steps, we obtained an average error of 0.79 MBT score points (9% error). Using Stopping Criterion 2, the average prediction error decreased with the confidence level at the cost of an increased average number of tasks (see Table 2). Setting the confidence level to 92.5%, an average error of 0.4 MBT score points (3.31% error) was obtained while requiring an average of four tasks to be performed per subject.