# Using Machine Learning to Shorten and Adapt Fall Risk Assessments for Older Adults
## 6 Conclusions  
In this study, we showed that balance testing, which is typically lengthy and time demanding of the medical professional administering the exam, can be efficiently and effectively shortened to form a testing sequence adapted to each individual. In numerous models, the ML-CAT algorithm was shown to predict balance task scores with a high accuracy. We showed that, for all models, the accuracy of prediction increases with the number of tasks administered. We also showed a high predictive accuracy for models using all features as well as limiting features to feasible tasks. When using all features to predict the BBS and MBT, 4-6 tasks sufficed to obtain 91-96% accuracy in prediction. When restricting the features to feasible tasks, the BBS prediction remained the same, as most of the tasks were feasible; however, the MBT prediction reduced in accuracy to 84%, as many of the MBT tasks were excluded, and the feasible tasks were not as good predictors of the MBT. We further showed that non-feasible tasks could be well predicted using a few feasible tasks. For the TUG, 48 BT, and 4 MW, three feasible tasks sufficed to obtain 86-88% accuracy. When considering the initial tasks in the models in Results Part C, it could be seen that the most predictive tasks were similar among the models; specifically, the STS task was consistently found to be highly predictive (Table 4). This was unsurprising, as this group of tasks showed a high correlation [34]. For the BBS and MBT tests, each of which include 14 tasks, it was shown (Figures 4 and 7) that the original testing sequence is not preserved under the ML-CAT algorithm. It is also noteworthy to mention that the most predictive feature (chosen as the first task) in both the BBS and MBT predicting models was neither the easiest nor the most difficult task in the test. This was not surprising because, similar to binary search algorithms, extreme pivots are usually not efficient. In terms of the practical implementation of the ML-CAT algorithm, it was presented with model training and testing performed during the assessment (Step 2 in Section 3.3). This may be time consuming and impractical for real-time implementation. However, similarity between individuals implies similar ML-CAT-produced test sequences. Thus, by pre-processing the data using ML-CAT, we can produce a tree of tasks similar to a decision tree for which each subject traverses according to their scores on the tasks. The parameters of the models may be stored and used only for inference to predict the confidence at each step, as well as to predict the final score. Additionally, the system can be integrated into existing healthcare platforms, enabling seamless use by healthcare professionals. To ensure scalability, the algorithm can be deployed on secure cloud-based platforms, allowing for real-time processing for large populations while maintaining data security and privacy. An inherent limitation of the proposed approach is that the excluded tasks might serve additional functions that were not considered in this study. For example, specific tasks may guide healthcare professionals in assigning the subject with specific rehabilitation or strengthening exercises. Additionally, when implementing the system, care must be taken to ensure that the subject is positioned well within the camera frame and facing the appropriate direction for task assessment. This can be easily achieved by informed instructions being distributed by the test administrator or by an automated response if the system is automated. In summary, this study contributes to the field of computerized adaptive testing. The approach can be applied to any battery of tests that output a single final score, such as the functional capacity evaluation [37], activities of daily living [38], and the functional independence measure [39]. In this paper, the approach was implemented for the balance assessment of older adults, as this is one of the important assessments performed during rehabilitation.