## 4 Discussion  
In the design of a good multi-label classifier, it is important to consider not only classification performance but also statistical type errors. Since the \(F_{1}\) score is the average value of depression and anxiety, different combinations of the best algorithms in the single-label classifiers can be selected depending on which symptom is more focused, referencing confusion matrices.  
As mentioned in Section 1, there have been studies to reduce the number of effective questionnaires in geriatric assessment scales [7; 8]. We showed a novel method using machine learning to systematically find the minimal set of questionnaires for both depression and anxiety. It is a method devised to increase the accuracy of classification models, but since the number of questionnaires can be reduced, it may be helpful for convenient application in the clinical field. Additionally, some K-SGDS and K-GAI questionnaires are still required for practical performance in the field. The best classifiers for depression and anxiety have to be re-selected if the data is changed.  
Motivated by the work of [PERSON] et al. [10], we developed an accurate multi-label classification model for identifying geriatric depression and anxiety using both low-cost activity trackers and minimal set of assessment scale data. They used additional input data including electrodermal activity (EDA), location changes, phone-based communication and phone usage patterns. [PERSON] et al. [17] and [PERSON] et al. [18] achieved a successful work in developing a classification model for depression using digital biomarkers such as steps, heart rate, energy expenditure and sleep data from consumer-grade wearable sensors. Their work is limited to finding depression only, and, to the best of authors' knowledge, there have been no other machine learning-based studies to identify the comorbid problem of geriatric depression and anxiety using a low-cost activity tracker. [PERSON] et al. [26] attempted an earlier development of a multi-label classifier using low-cost activity trackers. They showed the potential, but the prediction accuracy needs to be improved to be used in the field. This study extended the work of [PERSON] et al. [26] to improve single-label classification performances from less than 70% to higher than 90% by combining activity tracking and minimal assessment scale features. In addition, in this study, we adapted more appropriate performance metrics, namely the Hamming loss, which penalizes the prediction error to select the best performing model for multi-label classification.  
The limitations of this study are given as follows. First, we proposed minimal assessment scale features in addition to activity tracking features to classify geriatric mood disorders with high accuracy. In order to use only activity tracking data, one may need to use activity trackers with much higher resolutions such as ActiGraph at the cost of practicability. Second, we used the BR method without considering inter-label relationships between depression and anxiety. It would also be a good research topic to try different multi-label methods, such as the adapted algorithm, to consider the label relationships. Third, it is well-known that the seasonal changes have been associated with mood symptoms as well as the amount of physical activities and sleep [44]. Although we have not considered the effects of seasons on affective symptoms or physical activities in this study, it would be important to consider the effects of the seasonal variations on the development of symptoms in depression or anxiety. Further studies based on seasonal activity data would be warranted to investigate the effects of seasonal variations on the accuracy of identifying mood symptoms based on the data from activity trackers. Fourth, as can be seen in the learning curves, collecting and using more sample data will help reduce the variance of the model prediction.