Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model.
Journal:
Computational intelligence and neuroscience
PMID:
35378814
Abstract
In today's era, social networking platforms are widely used to share emotions. These types of emotions are often analyzed to predict the user's behavior. In this paper, these types of sentiments are classified to predict the mental illness of the user using the ensembled deep learning model. The Reddit social networking platform is used for the analysis, and the ensembling deep learning model is implemented through convolutional neural network and the recurrent neural network. In this work, multiclass classification is performed for predicting mental illness such as anxiety vs. nonanxiety, bipolar vs. nonbipolar, dementia vs. nondementia, and psychotic vs. nonpsychotic. The performance parameters used for evaluating the models are accuracy, precision, recall, and F1 score. The proposed ensemble model used for performing the multiclass classification has performed better than the other models, with an accuracy greater than 92% in predicting the class.