Adult Attention Deficit Hyperactivity Disorder Risk Detection Using Forward Chaining Method

Abstract

Adult Attention Deficit Hyperactivity Disorder (ADHD) remains underrecognized due to subtle symptoms, persistent misconceptions, and limited access to self-screening tools. This study developed a robust expert system to support the early detection of ADHD risk using the Forward Chaining method. Knowledge acquisition was conducted through literature review and consultation with a psychologist specializing in ADHD to construct a rule-based knowledge base of symptoms and risk classifications. The system provided three possible outcomes: high, moderate, or no risk. Evaluation was carried out through black-box testing with ten respondents and accuracy testing against expert diagnoses. The results showed that the system achieved 100% functionality and 100% accuracy in identifying ADHD risk levels. This research contributes to addressing the gap in digital screening platforms for adult ADHD in Indonesia by providing an accessible and user-friendly tool for self-assessment. The system can be a substitute for an early detection aid to consultation and reduce delays in intervention.