An AI-based Decision Support System for Predicting Mental Health Disorders

Area of Science:

• Computer Science
• Artificial Intelligence
• Mental Health

Background:

• Mental health disorders affect approximately one billion people globally.
• Current diagnostic tools are lengthy, leading to low completion rates and potential inaccuracies.
• Manual analysis of assessment results requires significant professional time.

Purpose of the Study:

• To develop an advanced decision support system (DSS) using artificial intelligence for efficient mental disorder detection and diagnosis.
• To create a streamlined assessment tool that enhances participation and completion rates.
• To improve diagnostic accuracy and clinical decision-making for mental health professionals.

Main Methods:

• Utilized the Network Pattern Recognition (NEPAR) algorithm to develop the assessment tool and select key questions.
• Trained machine learning models using participant answers and historical data for disorder prediction.
• Developed a DSS capable of automatic diagnosis without human input.

Main Results:

• The proposed DSS achieved an 89% accuracy level in diagnosing mental disorders.
• The assessment tool requires only 28 questions, significantly fewer than traditional methods.
• The system demonstrated higher participation and completion rates compared to existing tools.

Conclusions:

• The AI-driven DSS offers an efficient and accurate method for diagnosing mental health conditions.
• The reduced number of questions enhances patient engagement and data collection.
• This tool can support mental health professionals in making more informed clinical decisions.