Deep Learning-Based Risk Assessment of Depression Disease

Authors

  • Tushar Mehrotra Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Bhawna Wadhwa Assistant Professor, Department of Data Science (CS), Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Mahalakshmi Professor Department of Computer Science and Engineering, Presidency University, Bangalore, India
  • Manish kumar Goyal Assistant Professor, Department of Computer Science & Application, Vivekananda Global University, Jaipur, India

Keywords:

Depression, risk assessment, deep learning (DL), tuna swarm optimized attention-based long/short-term memory (TSO-ALSTM)

Abstract

Millions of people all around the world are affected by depression, a prevalent mental health disease. Effective treatments and assistance depends on the early identification and precise risk assessment of depression. In this study, we provide a unique tuna swarm-optimized attention-based long/short-term memory (TSO-ALSTM) approach based on deep learning (DL) to estimate the risk of depression. The attention mechanism helps the model concentrate on key features, and LSTM is renowned for its capacity to simulate long-term reliance on sequential information. The model's performance is enhanced by using the TSO method to optimize its settings. We make use of a dataset gathered from a Kaggle source to determine the risk of depression. Based on the suggested TSO-ALSTM approach, a number of performance metrics, including AUC, precision, accuracy, recall, and f1-score, are examined in relation to the risk assessment of depression disorder. Results from experiments show how well the suggested TSO-ALSTM model performs in correctly estimating the probability of depression. In regards to accurate forecasting and the value of features, it performs better than other models and conventional machine learning techniques.

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Published

11.07.2023

How to Cite

Mehrotra, T. ., Wadhwa, B. ., Mahalakshmi, & Goyal, M. kumar . (2023). Deep Learning-Based Risk Assessment of Depression Disease. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 461–467. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3075

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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