Glaucoma Detection Expert System Using Deep Learning and Certainty Theory

Glaucoma is a significant global health challenge, particularly for individuals in middle age with a family history of the disease. Effective management of glaucoma is crucial, and early detection can make a substantial difference in outcomes. The proposed expert system serves as a clinical decision support tool designed to assist individuals who have limited access to physicians and healthcare professionals in detecting glaucoma. This system adopts a patient-centric approach by gathering specific patient details to enhance detection accuracy. It integrates forward chaining as its core reasoning mechanism, ensuring that the expert system systematically analyzes patient data to arrive at conclusions. Beyond the traditional knowledge base, the system is augmented with a machine learning model trained on a comprehensive glaucoma dataset, enabling it to predict optimal treatment combinations tailored to individual needs. Additionally, the expert system leverages the generative capabilities of the latest GPT release, allowing users to engage in natural, dynamic dialogue to seek specific answers and gain deeper insights into their condition. This integration expands the scope of understanding and detection beyond what is possible with traditional methods. While the system is not intended to replace the expertise of healthcare professionals, it serves as a powerful tool to support and enhance clinical decision-making. By providing personalized and detailed medical plans, the system contributes to the more effective management of glaucoma, ultimately empowering both patients and medical professionals in the fight against this debilitating disease.