A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909752706990080 |
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| author | Ahmed, Md Sabbir Ahmed, Nova |
| author_facet | Ahmed, Md Sabbir Ahmed, Nova |
| contents | Background: Existing robust, pervasive device-based systems developed in recent years to detect depression require data collected over a long period and may not be effective in cases where early detection is crucial.
Objective: Our main objective was to develop a minimalistic system to identify depression using data retrieved in the fastest possible time.
Methods: We developed a fast tool that retrieves the past 7 days' app usage data in 1 second (mean 0.31, SD 1.10 seconds). A total of 100 students from Bangladesh participated in our study, and our tool collected their app usage data. To identify depressed and nondepressed students, we developed a diverse set of ML models. We selected important features using the stable approach, along with 3 main types of feature selection (FS) approaches.
Results: Leveraging only the app usage data retrieved in 1 second, our light gradient boosting machine model used the important features selected by the stable FS approach and correctly identified 82.4% (n=42) of depressed students (precision=75%, F1-score=78.5%). Moreover, after comprehensive exploration, we presented a parsimonious stacking model where around 5 features selected by the all-relevant FS approach Boruta were used in each iteration of validation and showed a maximum precision of 77.4% (balanced accuracy=77.9%). A SHAP analysis of our best models presented behavioral markers that were related to depression.
Conclusions: Due to our system's fast and minimalistic nature, it may make a worthwhile contribution to identifying depression in underdeveloped and developing regions. In addition, our detailed discussion about the implication of our findings can facilitate the development of less resource-intensive systems to better understand students who are depressed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18301 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach Ahmed, Md Sabbir Ahmed, Nova Machine Learning Computers and Society Human-Computer Interaction Background: Existing robust, pervasive device-based systems developed in recent years to detect depression require data collected over a long period and may not be effective in cases where early detection is crucial. Objective: Our main objective was to develop a minimalistic system to identify depression using data retrieved in the fastest possible time. Methods: We developed a fast tool that retrieves the past 7 days' app usage data in 1 second (mean 0.31, SD 1.10 seconds). A total of 100 students from Bangladesh participated in our study, and our tool collected their app usage data. To identify depressed and nondepressed students, we developed a diverse set of ML models. We selected important features using the stable approach, along with 3 main types of feature selection (FS) approaches. Results: Leveraging only the app usage data retrieved in 1 second, our light gradient boosting machine model used the important features selected by the stable FS approach and correctly identified 82.4% (n=42) of depressed students (precision=75%, F1-score=78.5%). Moreover, after comprehensive exploration, we presented a parsimonious stacking model where around 5 features selected by the all-relevant FS approach Boruta were used in each iteration of validation and showed a maximum precision of 77.4% (balanced accuracy=77.9%). A SHAP analysis of our best models presented behavioral markers that were related to depression. Conclusions: Due to our system's fast and minimalistic nature, it may make a worthwhile contribution to identifying depression in underdeveloped and developing regions. In addition, our detailed discussion about the implication of our findings can facilitate the development of less resource-intensive systems to better understand students who are depressed. |
| title | A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach |
| topic | Machine Learning Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.18301 |