Prototype Protection Mobile: AI-Powered Mental Health Screening for Building Inclusive Campus
Abstract
Adolescent mental health issues require early detection to prevent worsening conditions. This study aim developed a rule-based expert system for automating mental health screening instrument interpretation using Forward Chaining inference and Certainty Factor for uncertainty handling. The system encodes interpretation guidelines from two validated instruments: Mini MindHEAR Youth Scale V.1 for ages 10-18 years and Self-Reporting Questionnaire-29 for ages 19-24 years. From 710 survey respondents, 494 representative samples were selected using Stratified Random Sampling for validation. The knowledge base consists of 17 rules for Mini MindHEAR Youth Scale V.1 and 8 rules for Self-Reporting Questionnaire -29 with Certainty Factor values ranging from 0.5 to 0.95 based on symptom severity. Validation results showed the system achieved an overall guideline-alignment accuracy of 89.68% (443 matching interpretations out of 494 samples), measuring the system's ability to faithfully reproduce instrument interpretation guidelines rather than clinical diagnostic accuracy. The system demonstrated high explainability through transparent reasoning traces. This expert system can assist healthcare workers in automating screening instrument interpretation, particularly in resource-limited settings
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