Mental Health Analyzer for Depression Detection Based on Textual Analysis

Bhat, P. Devadas; Anuse, Alwin; Kute, Rupali; Bhadade, R. S.; Purnaye, Prasad

doi:10.12720/jait.13.1.67-77

Cited by 5 publications

(2 citation statements)

References 0 publications

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“…With the development of social media, people started sharing their emotions and feelings by posting their messages on different platforms like Facebook, “X, formerly Twitter”, WhatsApp, etc. Therefore, to diagnose and control depression, several optimized machine and deep learning algorithms were developed using social media data [ 6 , 24 , 29 ]. Many resources restrict access to other users, so data must be shared in a centralized location for privacy and sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram

Gupta,

Khullar,

Goyal

et al. 2023

Diagnostics

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In this day and age, depression is still one of the biggest problems in the world. If left untreated, it can lead to suicidal thoughts and attempts. There is a need for proper diagnoses of Major Depressive Disorder (MDD) and evaluation of the early stages to stop the side effects. Early detection is critical to identify a variety of serious conditions. In order to provide safe and effective protection to MDD patients, it is crucial to automate diagnoses and make decision-making tools widely available. Although there are various classification systems for the diagnosis of MDD, no reliable, secure method that meets these requirements has been established to date. In this paper, a federated deep learning-based multimodal system for MDD classification using electroencephalography (EEG) and audio datasets is presented while meeting data privacy requirements. The performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. The study began by extracting features from several pre-trained models and ultimately decided to use bidirectional short-term memory (Bi-LSTM) as the base model, as it had the highest validation accuracy of 91% compared to a convolutional neural network and LSTM with 85% and 89% validation accuracy on audio data, respectively. The Bi-LSTM model also achieved a validation accuracy of 98.9% for EEG data. The FL method was then used to perform experiments on IID and non-IID datasets. The FL-based multimodal model achieved an exceptional training and validation accuracy of 99.9% when trained and evaluated on both IID and non-IIID datasets. These results show that the FL multimodal system performs almost as well as the Bi-LSTM multimodal system and emphasize its suitability for processing IID and non-IIID data. Several clients were found to perform better than conventional pre-trained models in a multimodal framework for federated learning using EEG and audio datasets. The proposed framework stands out from other classification techniques for MDD due to its special features, such as multimodality and data privacy for edge machines with limited resources. Due to these additional features, the framework concept is the most suitable alternative approach for the early classification of MDD patients.

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Section: Introductionmentioning

confidence: 99%

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram

Gupta,

Khullar,

Goyal

et al. 2023

Diagnostics

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“…[1]. Many needed assistance during the lockdown because of the isolation, physical separation, and shutdown of businesses which resulted in stress, worry, fear, loneliness, and even melancholy [2]. Anxiety disorders, agitation, insomnia, eating disorders, addiction disorders, depression, traumatic stress disorders, and stress-related illnesses are the most prevalent cases of mental health problems.…”

Section: Introductionmentioning

confidence: 99%

Depression Identification Using Machine Learning Classifiers

Srivastava,

Pandey,

Gupta

et al. 2023

IJIRCST

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Depression is a mental condition that indicates emotional issues, including anger issues, unhappiness, boredom, appetite loss, lack of concentration, anxiety, etc. The quality of life of an individual may be negatively impacted by depression, which may ultimately lead to loss of health and life. According to the World Health Organization, there are 300 million depressed persons worldwide in 2022. The number of depression cases rose throughout the pandemic. It became important to detect depression in people accurately. During the construction of the model various machine learning techniques were applied. Support Vector Machine (SVM), Random Forest, Naive Bayes, K Nearest Neighbour (KNN), and Logistic Regression were used to test the accuracy of the model. Among all techniques, Logistic Regression had the highest accuracy. The proposed technique improved the accuracy of 0.79 in comparison with the other existing state of art. Physical health and mental health, both are equally important. Early detection of depression is necessary so that it can be treated in its early stage.

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