Expert judgment, limitation inference, and threshold values to optimize diagnosis in eye diseases expert system

Abstract

This research aimed to develop an optimal expert system by adopting a simplified approach. The methodology integrates an expert judgment approach, limitation inference, and establishing a threshold value. Expert judgment is pivotal in assigning a percentage weight to each rule, facilitating a nuanced evaluation of diagnostic criteria to augment the system's precision. Moreover, incorporating limitation inference strategically constrains the number of user inquiries, streamlining the diagnostic process and enhancing overall efficiency. Additionally, the imposition of a threshold value ensures a more precise early diagnosis by delineating specific criteria for condition identification. This comprehensive approach underscores the paramount importance of user experience and aims to alleviate the burden on individuals seeking a diagnosis. Ultimately, the anticipated outcome of this study is the development of an expert system poised to deliver early diagnoses with heightened efficiency and accuracy. By integrating expert judgment, limitation inference, and threshold values, this research embodies a refined and user-centric paradigm for eye disease diagnosis, promising significant advancements in global eye health.