AUTOMATED DEPRESSION SCREENING OF CLINICAL TRANSCRIPT OPTIMIZED BY GREY WOLF OPTIMIZER | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Volume 24, Issue 3, September 2024, Page 58-69 PDF (453.04 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2024.319767.1354 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Alwan Atta   1; Dina Elsayad 2; Doaa Ezzat3; Safaa Amin4; Mahmoud ElGamal3
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| 1Ain Shams University, and Galala University | ||||
| 2Faculty of computer and Information Sciences,Ain Shams University | ||||
| 3Ain Shams University | ||||
| 4Faculty of computer and information sciences, Ain shams university | ||||
| Abstract | ||||
| Depression diagnosis depends on the transcripts obtained from clinical interviews during mental health assessment. This hybrid model proposes Decision Tree (DT) and Grey Wolf Optimizer (GWO) for feature selection to enhance depression detection. The proposed model makes use of the Decision Tree, thereby effectively grasping the temporal context embedded in clinical interview transcripts. It can detect significant linguistic features of depressive symptoms. This model will also include the integration of GWO to optimize feature selection for added strength and efficiency. In this way, a hybrid model featuring a high F-score macro of 0.83 could be derived, which proved its effectiveness in detecting depression with accuracy. This will contribute to the literature by enhancing computational methods for measuring mental health, and this model has immense potential to apply in clinical practice. Beyond academic research, the applications of such a hybrid model are huge and promising in a clinical setting. The model automates the diagnosis of depression to assist healthcare professionals in informed and timely intervention. | ||||
| Keywords | ||||
| Swarm Intelligence; Decision Tree; Clinical Interviews; Automated Depression Diagnosis | ||||
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