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Tackling Depression Detection With Deep Learning: A Hybrid Model

Tackling Depression Detection With Deep Learning: A Hybrid Model

N. Bala Krishna, Reddy Sai Vikas Reddy, M. Likhith, N. Lasya Priya
Copyright: © 2024 |Pages: 16
ISBN13: 9798369336793|EISBN13: 9798369336809
DOI: 10.4018/979-8-3693-3679-3.ch006
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MLA

Krishna, N. Bala, et al. "Tackling Depression Detection With Deep Learning: A Hybrid Model." Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications, edited by Alex Khang, IGI Global, 2024, pp. 102-117. https://doi.org/10.4018/979-8-3693-3679-3.ch006

APA

Krishna, N. B., Vikas Reddy, R. S., Likhith, M., & Priya, N. L. (2024). Tackling Depression Detection With Deep Learning: A Hybrid Model. In A. Khang (Ed.), Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications (pp. 102-117). IGI Global. https://doi.org/10.4018/979-8-3693-3679-3.ch006

Chicago

Krishna, N. Bala, et al. "Tackling Depression Detection With Deep Learning: A Hybrid Model." In Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications, edited by Alex Khang, 102-117. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-3679-3.ch006

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Abstract

Traditionally, PHQ scores and patient interviews were used to diagnose depression; however, the accuracy of these measures is quite low. In this work, a hybrid model that primarily integrates textual and audio aspects of patient answers is proposed. Using the DAIC-WoZ database, behavioral traits of depressed patients are studied. The proposed method is comprised of three parts: a textual ConvNets model that is trained solely on textual features; an audio CNN model that is trained solely on audio features; and a hybrid model that combines textual and audio features and uses LSTM algorithms. The suggested study also makes use of the Bi-LSTM model, an enhanced variant of the LSTM model. The findings indicate that deep learning is a more effective method for detecting depression, with textual CNN models having 92% of accuracy and audio CNN models having 98% of accuracy. Textual CNN loss is 0.2 while audio CNN loss is 0.1. These findings demonstrate the efficacy of audio CNN as a depression detection model. When compared to the textual ConvNets model, it performs better.

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