Enhancing Mental Health Disorders Classification UsingConvolutional Variational Autoencoder
Dublin Core
Title
Enhancing Mental Health Disorders Classification UsingConvolutional Variational Autoencoder
Subject
CVAE, Mental Health, Classification, Deep Learning
Description
This research investigates the application of Convolutional Variational Autoencoder (CVAE) for multi-class classification of mental health disorders. The study utilizes a diverse dataset comprising five classes: Normal, Anxiety, Depression, Loneliness, and Stress. The CVAE model effectively captures spatial dependencies and learns latent representations from the mental health disorder data. The classification results demonstrate high precision, recall, and F1 scores for all classes, indicating the model's robustness in distinguishing between different disorders accurately. The research contributes by leveraging the unique capabilities of CVAE, combining convolutional neural networks and variational autoencoders to enhance the accuracy and interpretability of the classification process. The findings highlight the potential of CVAE as a powerful tool for accurate and efficient mental health disorder classification. This research paves the way for further advancements in deep learning techniques, supporting improved diagnosisand personalized healthcare in mental health
Creator
Sri Hasta Mulyani1, Mohammad Diqi2,Annisa Rahmidini3, Erlin Ifadah4, I Wayan Ordiyasa5, Marselina Endah Hiswati
Source
https://ijicom.respati.ac.id/index.php/ijicom/article/view/65/53
Date
August 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Sri Hasta Mulyani1, Mohammad Diqi2,Annisa Rahmidini3, Erlin Ifadah4, I Wayan Ordiyasa5, Marselina Endah Hiswati, “Enhancing Mental Health Disorders Classification UsingConvolutional Variational Autoencoder,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8393.