###### Abstract  
The identification of geriatric depression and anxiety is important because such conditions are the most common comorbid mood problems that occur in older adults. The goal of this study was to build a machine learning framework that identifies geriatric mood disorders of depression and anxiety using low-cost activity trackers and minimal geriatric assessment scales. We collected activity tracking data from 352 mild cognitive impairment patients, from 60 to 90 in age, by having them wear activity trackers on their wrist for more than a month. We then extracted the features of 24-h activity rhythms and sleep patterns from the time-series activity tracking data. To increase the accuracy, we designed a novel method to incorporate additional features from questionnaire-based assessments of the geriatric depression scale and geriatric anxiety inventory into the activity tracking features. In the multi-label classification, we applied the binary relevance method to develop two single-label classifiers for depression and anxiety. The best hyper-parameters of classification algorithms for each label were selected by comparing the classification performance. We finally selected the combination of classifiers for depression and anxiety with the lowest Hamming loss as a multi-label classifier. This study successfully demonstrated the possibility of identifying geriatric depression and anxiety using low-cost activity trackers and minimal geriatric assessment scales for use in the real fields. definition of Geriatric Depression and Anxiety Using  
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management of such mood symptoms could be important in mitigating further cognitive impairment in older adults. It is challenging for physicians to diagnose patients accurately who present with depression and anxiety. Anxiety and depression have distinct psychological features. Given that patients showed distinct symptoms and need different medical management according to the diagnosis of depression or anxiety in clinical practice, clinicians should differentiate whether patients had depression or anxiety accurately. Clinicians typically use the following self-reporting questionnaires to assess the major geriatric mood disorders: the geriatric depression scale (GDS) for depression symptoms [5] and geriatric anxiety inventory (GAI) for anxiety symptoms [6]. Both GDS and GAI are composed of questionnaires with yes or no questions that are answered by the patients or their families. Based on the assessment results, a patient can be diagnosed as either depressed or anxious, or a combination of both. However, self-reporting methods such as GDS and GAI may contain incorrect information because they rely on answers received from the patients or their families. Additionally, the large number of answers required by such questionnaires can be inconvenient to the patients. To reduce these limitations, there have been efforts to reduce the number of questionnaires. [PERSON] et al. verified that the five items in the GDS short form are statistically significant even though the form has a total of fifteen questionnaires [7]. [PERSON] et al. demonstrated the possibility of using GAI questionnaires with five items instead of twenty [8]. Instead of such self-reporting methods, an accurate diagnosis requires complex procedures using expensive equipment such as functional magnetic resonance imaging (fMRI). In assessing geriatric mood disorders, more convenient, relatively low-cost and reliable systematic approaches through direct measurements are needed. An increased risk of mood disorders are known to be associated with disorders of Circadian rhythms, endogenous rhythms with a periodicity of approximately 24 h and sleep [9]. In fact, the patterns of 24-h activity rhythms and sleep can be extracted from time-series activity tracking data from activity trackers [10], such as ActiGraph [11] and Fitbit [12], which are increasingly used in clinical and daily life these days.