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Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment.

Dillan Imans¹, Tamer Abuhmed¹, Meshal Alharbi²

¹College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, Republic of Korea.

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|November 9, 2024

Summary

This summary is machine-generated.

This study introduces an explainable AI framework using dynamic ensemble learning for accurate depression detection and severity assessment in older adults. The model achieved high accuracy, highlighting key health factors for improved mental health diagnostics.

Keywords:

classifier optimization depression detection dynamic ensemble explainable AI machine learning

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Area of Science:

Artificial Intelligence in Healthcare
Machine Learning for Mental Health
Geriatric Psychiatry

Background:

Depression significantly impacts older adults, necessitating early detection and intervention.
Existing diagnostic methods may lack precision and interpretability.
This study addresses the need for advanced tools in geriatric mental health assessment.

Purpose of the Study:

To develop and evaluate an explainable multi-layer dynamic ensemble framework for depression detection and severity assessment.
To improve diagnostic accuracy and provide insights into contributing health factors for depression in older adults.
To enhance the clinical applicability of AI in mental health through interpretable models.

Main Methods:

Utilized data from the National Social Life, Health, and Aging Project (NSHAP).
Employed a two-stage framework combining classical ML, static ensembles, and dynamic ensemble selection (DES).
Integrated Explainable AI (XAI) techniques for model interpretability.

Main Results:

The FIRE-KNOP DES algorithm achieved 88.33% accuracy in depression detection and 83.68% in severity prediction.
XAI analysis identified significant mental and non-mental health indicators influencing depression assessment.
The framework demonstrated high efficacy in classifying depression and its severity.

Conclusions:

Dynamic ensemble learning shows significant potential for mental health assessments, particularly for depression.
The developed framework offers a robust foundation for practical clinical applications in mental health.
Explainable AI enhances the utility of machine learning models in diagnosing and assessing depression severity.