EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network

Abstract

Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and a hybrid Long Short-Term Memory network (LSTM) model. The feature extraction process combines a novel feature selection method called Elite Term Score (ETS) and Word2Vec to extract the syntactic and semantic information respectively. First, the ETS method leverages the document level, class level, and corpus level probabilities for computing the weightage/score of the terms. Then, the ideal and pertinent set of features with a high ETS score is selected, and the Word2vec model is trained to generate the intense feature vector representation for the set of selected terms. Finally, the resultant word vector obtained is called EliteVec, which is fed to the hybrid LSTM model based on Honey Badger optimizer with population reduction technique (PHB) which predicts whether the input textual content is depressive or not. The PHB algorithm is integrated to explore and exploit the optimal hyperparameters for strengthening the performance of the LSTM network. The comprehensive experiments are carried out with two different Twitter depression corpus based on accuracy and Root Mean Square Error (RMSE) metrics. The results demonstrated that the proposed EliteVec+LSTM+PHB model outperforms the state-of-art models with 98.1% accuracy and 0.0559 RMSE.

Enhancing Depression Predictive Models: a Comparative Study of Hybrid Ai, Machine Learning and Deep Learning Techniques

“…Their study evaluates the diagnostic accuracy of GDS-30, GDS-15, GDS-5, and GDS-4 across a range of situations and demographics, overcoming the drawbacks of earlier investigations, such as selective reporting and a lack of subgroup analysis. By taking into consideration variables like age, sex, dementia status, and care setting, this allencompassing approach offers more sophisticated and customised screening procedures that may improve the diagnosis of depression in older people.Using a hybrid pBGSK optimisation method, Kavi Priya and Pon Karthika (2023) [11] presented a unique multi-objective feature selection model for depression diagnosis. By using advanced computational algorithms to identify depressive symptoms, this methodology improves the e ciency and accuracy of depression detection systems by optimising feature selection.…”

Enhancing multimodal depression diagnosis through representation learning and knowledge transfer

“…Many researchers have obtained effective results in depression diagnosis from various data sources. S. K. Priya et al [ 19 ] employ natural language processing techniques and machine learning models to extract features from textual data for the identification of depression. Y. Guo et al [ 20 ] conducted research on diagnosing depression from video data.…”