Abstract

Approximately 1 billion individuals worldwide have mental disorders such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health practitioners use a range of screening instruments to identify and diagnose these disorders. Such instruments are, however, complicated, contain too many questions, and take a lot of time to administer, resulting in low levels of response and completion. Moreover, manual analysis and interpretation of results by mental health professionals may lead to inaccurate diagnoses. To address these issues, this research employs sophisticated analyses and artificial intelligence to create a decision-making system (DSS) to efficiently detect and diagnose mental disorders. The development process starts with the network recognition algorithm (NPAR), which constructs an assessment tool and identifies the most suitable questions for respondents. Then, the models of automatic learning are developed based on the participant's responses and the past data to make predictions regarding the presence as well as the nature of the mental disorder.

 

The findings show that the suggested DSS can be used to identify mental disorders accurately at a level of 89%, with 28 questions alone, without depending on an individual's contribution. In comparison with conventional diagnostic materials, the suggested system significantly reduces the number of issues, thereby resulting in higher participation and outcomes. Consequently, mental health specialists can utilize the suggested DSS and its enhanced assessment tool for improved clinical decision-making and augmented diagnostic accuracy.

Keywords

  • Mental Health Diagnostics
  • Natural Language Processing

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