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Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption
Jaskaran Singh1, Narpinder Singh2, Mostafa M Fouda3
1Department of Computer Science, Graphic Era, Deemed to be University, Dehradun 248002, India.
Diagnostics (Basel, Switzerland)
|June 28, 2023
Summary
Attention-enabled ensemble deep learning (aeEDL) models significantly improve depression detection accuracy across different domains. These advanced models outperform traditional methods, offering a more generalized and effective approach for identifying depression symptoms.
Area of Science:
- Computational linguistics and natural language processing.
- Artificial intelligence and machine learning applications in healthcare.
- Psychiatric informatics and computational psychiatry.
Background:
- Depression is a growing global health concern with increased suicide risks.
- Accurate depression detection using text analysis in cross-domain settings remains a significant challenge.
- Existing solo deep learning (SDL) and ensemble deep learning (EDL) models lack sufficient robustness.
Purpose of the Study:
- To investigate the efficacy of attention-enabled ensemble deep learning (aeEDL) architectures for depression detection.
- To compare the performance of aeEDL against attention-not-enabled SDL (aneSDL), attention-enabled SDL (aeSDL), and attention-not-enabled EDL (aneEDL) models.
- To validate the generalizability and effectiveness of aeEDL in cross-domain sentiment analysis for depression detection.
Main Methods:
- Development of EDL-based architectures incorporating attention blocks for both SDL and EDL models.
- Training and evaluation of eleven SDL and five EDL models on four domain-specific datasets.
- Scientific validation using 'seen' and 'unseen' paradigms (SUP) and benchmarking against the SemEval (2016) dataset.
Main Results:
- EDL models showed a mean accuracy increase of 4.49% over corresponding SDL components.
- Attention mechanisms improved mean accuracy (AUC) by 2.58% (1.73%) for aeSDL over aneSDL and 2.76% (2.80%) for aeEDL over aneEDL.
- aeEDL models consistently outperformed SDL counterparts, with the best aeEDL (ALBERT+BERT-BiLSTM) surpassing the best aeSDL (BERT-BiLSTM) by 3.86% on the SemEval dataset.
Conclusions:
- Attention-enabled ensemble deep learning (aeEDL) architectures are superior for depression detection in cross-domain settings.
- The proposed aeEDL method demonstrates high effectiveness and generalizability, meeting stringent validation criteria.
- This research validates the hypothesis that incorporating attention mechanisms into EDL models significantly enhances depression symptom detection.
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