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. Additionally, due to the requirement of running systems in the background for prolonged periods, existing systems can be resource inefficient. As a result, these systems can be infeasible in low-resource settings.

Objective

Our main objective was to develop a minimalistic system to identify depression using data retrieved in the fastest possible time. Another objective was to explain the machine learning (ML) models that were best for identifying depression.

Methods

We developed a fast tool that retrieves the past 7 days' app usage data in 1 second (mean 0

SUBMITTER: Ahmed MS 

PROVIDER: S-EPMC10450542 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature