Automated detection of human mental disorder

The pressures of daily life result in a proliferation of terms such as stress, anxiety, and mood swings. These feelings may be developed to depression and more complicated mental problems. Unfortunately, the mood and emotional changes are difficult to notice and considered a disease that must be tre...

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Bibliographic Details
Published in:Journal of Electrical Systems and Information Technology 2023-12, Vol.10 (1), p.9-10, Article 9
Main Authors: Hussein, Shereen A., Bayoumi, Abd El Rahman S., Soliman, Ayat M.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Anxiety
Anxiety disorders
Artificial intelligence
Behavior
Classification
CNN
Datasets
Depression
Emotions
Engineering
Experiments
Facial emotions
Feature extraction
Machine learning
Mechanical Engineering
Mental depression
Mental disorders
Mental health
Neural networks
Stress
VGG16
Citations: Items that this one cites
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Summary:The pressures of daily life result in a proliferation of terms such as stress, anxiety, and mood swings. These feelings may be developed to depression and more complicated mental problems. Unfortunately, the mood and emotional changes are difficult to notice and considered a disease that must be treated until late. The late diagnosis appears in suicidal intensions and harmful behaviors. In this work, main human observable facial behaviors are detected and classified by a model that has developed to assess a person’s mental health. Haar feature-based cascade is used to extract the features from the detected faces from FER+ dataset. VGG model classifies if the user is normal or abnormal. Then in the case of abnormal, the model predicts if he has depression, anxiety, or other disorder according to the detected facial expression. The required assistance and support can be provided in a timely manner with this prediction. The system has achieved a 95% of overall prediction accuracy.
ISSN:2314-7172
2314-7172
DOI:10.1186/s43067-023-00076-3