# Using Machine Learning to Shorten and Adapt Fall Risk Assessments for Older Adults
## 3 Methods
### ML-CAT Model  
The approach used in this study is a computerized adaptive testing (CAT) scheme [28] that is based on machine learning (ML), thus termed ML-CAT. Similar to the approach \begin{table}
\begin{tabular}{l c c} \hline \hline
**Characteristic** & **Dataset 1** & **Dataset 2** \\ \hline Age in years: mean (SD) & 73.2 (4.0) & XXX \\ Sex: female (\%) & 190 (74.5) & XXX \\
**Physical measures: mean (SD)** & & \\ BBS (range 0–56) & 38.8 (12.5) & 54.7 (1.5) \\ MBT (range 0–28) & - & 25.1 (2.0) \\ TUG (s) & - & 10.9 (3.4) \\
4 SBT (s) & - & 12.0 (7.1) \\
4 MW (m/s) & - & 3.3 (0.5) \\ STS (s) & - & 14.0 (4.4) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Baseline characteristics and assessments of Datasets 1 and 2.  
in [22], the ML-CAT algorithm selects a different sequence of tasks to be performed for each subject by iteratively choosing the next task in the sequence based on the scores obtained by the subject on previous tasks. The ML-CAT algorithm is trained using a database of samples, with our study using Dataset 1 or Dataset 2. Each sample in the dataset is a list of \(N\) features consisting of task scores associated with a subject. Denote the features (task scores) of a sample as \(S_{j}\) for \(j=\{1,\ldots,N\}\). Assume that the goal of the ML-CAT algorithm is to predict a score \(S\) (e.g., the final BBS score or the final Mini-Best score) of an incoming subject. The ML-CAT algorithm initializes with the subject's score on a single predetermined first task. Iteratively, the ML-CAT algorithm considers the scores accumulated by the subject and predicts the next task to be administered. Assume that the subject has performed \(k\) tasks (\(k\leq N\)) \(T_{1},\ldots,T_{k}\) and has obtained scores \(S_{1},\ldots,S_{k}\), respectively; then, the next iteration of the ML-CAT algorithm proceeds as follows (see Figure 1):  
1. Determine the samples in the database that are most similar to the subject in the scores \(S_{1},\ldots,S_{k}\). Collect the D% most similar samples. 2. For each task \(T\) that is not among the \(k\) tasks that the subject has completed, train an ML model on the similar samples selected in Step 1, using as input features their scores on tasks \(T_{1},\ldots,T_{k}\) and \(T\). 3. Test the accuracy of all models trained in Step 2 in predicting the score \(S\) on similar samples. 4. Choose the next task \(T\) to be administered as that for which the model accuracy is the highest. 5. Administer task \(T\) to the subject. Denote this task \(T_{k+1}\) and the score \(S_{k+1}\). 6. If the algorithm's stopping criteria have not been reached and there is a task that has not yet been administered, then return to Step 1 with \(S_{1},\ldots,S_{k},S_{k+1}\). 7. Otherwise, use \(S_{1},\ldots,S_{k},S_{k+1}\) to predict the score \(S\) using the model with the highest accuracy (from Step 4). Report the results. The algorithm is similar in spirit to that in [22], with several significant differences that improve performance and allow for a more practical implementation for real-world use. The training of the models in [22] was performed using a training set selected a priori so that all models were trained on the same set. In our approach, the training set is adapted to the subject being tested, so the models are better tuned to predict the scores of each subject. Figure 1: Schematic diagram of the adaptive ML-CAT model for predicting the score in balance tests. In Method 3 in [22], model accuracy was assessed by weighting the training samples by the similarity of their scores to the scores of the subject. Samples that were not similar were not weighted strongly. However, they still affected the sum that determined the next task. In our approach, the samples that are not similar to the subject are disregarded, both in training and in testing. Thus, dissimilar samples do not have an effect on the outcome. In [22], Method 4 considered samples for which the trained model correctly predicted the final outcome.