Early Detection and Analysis of Anxiety in Adolescents
Mental health has become one of the most important topics in today’s world and has been included in WHO’s World Sustainable Development goals. Significant breakthrough made in diagnosis and treatment has reduced the mortality caused by various diseases, however depression and suicide caused by menta...
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Veröffentlicht in: | Mathematical models and computer simulations 2023-10, Vol.15 (5), p.956-967 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Mental health has become one of the most important topics in today’s world and has been included in WHO’s World Sustainable Development goals. Significant breakthrough made in diagnosis and treatment has reduced the mortality caused by various diseases, however depression and suicide caused by mental health disorders has become the leading cause of death in the world. Though mental health treatment is easily available and inexpensive, social stigma prevents people from coming forward for detection and treatment. To combat this growing epidemic, this paper describes an effective noninvasive two-step method which helps in early detection of anxiety in children below age of 19. This involves cognitive behavioral analysis from the data taken from both the child and parent. The score derived from the above is used to decide the requirement for further analysis. EEG is used to check the brain state. A machine learning classifier algorithm is used to classify the EEG data and determine anxiety. This helps in confirming anxiety in children who need psychiatrist support for further treatment. |
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ISSN: | 2070-0482 2070-0490 |
DOI: | 10.1134/S2070048223050150 |