UTILIZING FUZZY EXPERT SYSTEM TO AID IN DIAGNOSING HEART DISEASES AT SABRATA HOSPITAL, LIBYA

Medical Computer Engineering

Heart disease remains a leading global health challenge, particularly in resource-limited regions like Sabrata, where timely and accurate diagnosis is often hindered by limited access to skilled cardiologists and advanced diagnostic equipment. This study presents the development and evaluation of a fuzzy expert system tailored to aid healthcare providers at Sabrata Hospital in diagnosing heart conditions effectively. By leveraging fuzzy logic, the system addresses the inherent uncertainties in medical data, such as vague symptom descriptions and incomplete records, to deliver accurate diagnostic predictions. The system integrates inputs like patient symptoms, medical history, and diagnostic test results, converting them into fuzzy values for analysis. Through fuzzification, inference, and defuzzification processes, the system generates crisp outputs to support clinical decision-making. Tools like Python, HTML, CSS, ECG, echocardiography, and angiography are utilized to implement and validate the system. Evaluation of the fuzzy expert system demonstrated enhanced diagnostic precision and sensitivity compared to conventional methods, reducing errors and expediting decision-making in complex cases. The findings underscore the potential of fuzzy expert systems in improving healthcare outcomes in underserved areas by offering cost-effective, reliable diagnostic solutions. Future research is recommended to incorporate machine-learning techniques to further refine and expand the system’s capabilities.

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