{"ID":2879588,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.18301","arxiv_id":"2508.18301","title":"A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach","abstract":"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.","short_abstract":"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 re...","url_abs":"https://arxiv.org/abs/2508.18301","url_pdf":"https://arxiv.org/pdf/2508.18301v1","authors":"[\"Md Sabbir Ahmed\",\"Nova Ahmed\"]","published":"2025-08-22T20:39:14Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CY\",\"cs.HC\"]","methods":"[\"LoRA\"]","has_code":false}