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Interpretation of Depression Detection Models via Feature Selection Methods.

Sharifa Alghowinem1, Tom Gedeon2, Roland Goecke3

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Summary
This summary is machine-generated.

This study introduces a novel framework for interpreting artificial intelligence models used in depression detection. The research found that specific speech behavior features, like pauses, are key indicators for accurately identifying depression with fewer data points.

Keywords:
datasets generalisationdepression detectionfeature selectionmultimodal analysis

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Area of Science:

  • Artificial Intelligence
  • Computational Psychiatry
  • Machine Learning

Background:

  • Depression is a global health concern with significant societal impact.
  • AI models are increasingly used for automated depression detection and assessment.
  • Interpreting AI models and identifying key features for depression detection remains a challenge.

Purpose of the Study:

  • To develop and evaluate a framework for feature selection to interpret AI-based depression detection models.
  • To identify the most influential behavioral cues for depression detection across various modalities.
  • To enhance the accuracy and efficiency of depression detection models through feature selection.

Main Methods:

  • A framework aggregating 38 feature selection algorithms was proposed.
  • 902 behavioral cues were extracted from speech, prosody, eye movement, and head pose from three real-world depression datasets.
  • The framework was applied to individual and combined datasets to assess generalizability.

Main Results:

  • Speech behavior features, particularly pauses, were identified as the most distinctive for depression detection.
  • Key features identified include F0, HNR, formants, MFCC (speech prosody); left-right eye movement, gaze direction (eye activity); and yaw head movement (head pose).
  • Models using the selected 9 features outperformed models using all features, achieving higher accuracy across datasets.

Conclusions:

  • The proposed feature selection framework provides model interpretability for AI-based depression detection.
  • A small set of selected features significantly improves depression detection accuracy and reduces processing time.
  • This approach offers a more efficient and understandable method for developing AI tools for mental health assessment.