Citation

Du C, Liu C, Balamurugan P, Selvaraj P. International Journal on Artificial Intelligence Tools 2021; 30(6-8).

Copyright

(Copyright © 2021)

DOI

10.1142/S0218213021400145

PMID

unavailable

Abstract

Artificial intelligence (AI) in healthcare has recently been promising using deep neural networks. It is indeed even been in clinical trials more and more, with positive outcomes. Deep learning is the process of using algorithms to train a neural network model using huge quantities of data to learn how to execute a given task and then make an accurate classification or prediction. Apart from physical health monitoring, such deep learning models can be used for the mental health evaluation of individuals. This study thus designs a deep learning-based mental health monitoring scheme (DL-MHMS) for college students. This model uses the most efficient convolutional neural network (CNN) to classify the mental health status as positive, negative, and normal using the EEG signals collected from college students. The simulation analysis achieves the highest classification accuracy and F1 scores of 97.54% and 98.35%, less sleeping disorder rate of 21.19%, low depression level of 18.11%, reduced suicide attention level of 28.14%, increasing personality development ratio of 97.52%, enhance self-esteem ratio of 98.42%, compared to existing models. © 2021 World Scientific Publishing Company

Language: en

Keywords

College students; Students; Mental health; Health; college students; Clinical trial; Learn+; neural networks; Artificial intelligence; Convolutional neural networks; Deep learning; deep learning; Deep neural networks; Neural network model; Convolution; Convolutional neural network; Health monitoring; mental health monitoring; Mental health monitoring; Neural-networks