# A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning-Based Approach

arXiv: https://arxiv.org/abs/2508.18301 · HTML: https://mlsanity.com/paper/2508.18301/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 104 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Instagram photos reveal predictive markers of depression](https://mlsanity.com/paper/1608.03282/) (2016)
- ['I Just Want to Hack Myself to Not Get Distracted': Evaluating Design Interventions for Self-Control on Facebook](https://mlsanity.com/paper/2001.04180/) (2020)
- [Understanding Perceptions of Problematic Facebook Use: When People Experience Negative Life Impact and a Lack of Control](https://mlsanity.com/paper/1905.01911/) (2019)

## Abstract (arXiv)

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.