An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders
- Title
- An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders
- Creator
- Vandana; Srivastava S.; Arora N.; Gupta V.
- Description
- Mental health disorders are primarily life style driven disorders, which are mostly unidentifiable by clinical or direct observations, but act as a silent killer for the impacted individuals. Using machine learning (ML), the prediction of mental ailments has taken significant interest in medical informatics community especially when clinical indicators are not there. But, majority studies now focus on usual machine learning methods used to predict mental disorders with few organized health data, this may give wrong signals. To overcome the drawbacks of the conventional ML prediction models, this work presents Deep Learning (DL) trained prediction model for automated feature extraction to realistically predict mental health disorders from the online textual posts of individuals indi cating suicidal and depressive contents. The proposed model encompasses three phases named pre-processing, feature extraction and optimal prediction phase. The developed model utilizes a novel Sparse Auto-Encoder based Optimal Bi-LSTM (SAE-O-Bi-LSTM) model, which integrates Bi-LSTM and Adaptive Harris-Hawk Optimizer (AHHO) for extracting the most relevant mental illness indicating features from the textual content in the dataset. The dataset utilized for training consist of 232074 unique posts from the "SuicideWatch" and "Depression" subreddits of the Reddit platform during December 2009 to Jan 2021 downloaded from Kaggle. In-depth comparative analysis of the testing results is conducted using accuracy, precisions, F1 score, specificity, and Recall and ROC curve. The results depict considerable improvement for our developed approach with an accuracy of 98.8% and precision of 98.7% respectively, which supports the efficacy of our proposed model. The Author(s) 2024.
- Source
- International Research Journal of Multidisciplinary Technovation, Vol-6, No. 4, pp. 106-123.
- Date
- 2024-01-01
- Publisher
- Asian Research Association
- Subject
- Adaptive Harris Hawk Optimizer; Adolescence; Emotion; Mental Health; Sparse Autoencoder
- Coverage
- Vandana, School of Sciences, Christ University, Bengaluru, India; Srivastava S., School of Sciences, Christ University, Bengaluru, India; Arora N., Department of Computer Science, Kalindi College, University of Delhi, Delhi, India; Gupta V., School of Sciences, Christ University, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISSN: 25821040
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Vandana; Srivastava S.; Arora N.; Gupta V., “An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/12985.