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@article{204107,
        author = {MS. K. PRIYANGA and Priya Nandagopal},
        title = {Analysis Of Rule-Based Expert Systems and Fuzzy Logic Systems for Heart Disease Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {9224-9237},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204107},
        abstract = {heart disease remains one of the leading causes of mortality worldwide, necessitating the development of accurate and intelligent diagnostic systems. Artificial Intelligence (AI) techniques have significantly enhanced healthcare decision support by enabling efficient disease prediction and diagnosis. This research presents a comparative analysis of a Rule-Based Expert System and a Fuzzy Logic Inference System for heart disease prediction. The research utilizes the UCI Heart Disease Dataset to evaluate the performance of both approaches. Patient attributes such as age, chest pain type, blood pressure, cholesterol level, fasting blood sugar, and maximum heart rate are used as input parameters. The Rule-Based Expert System employs predefined medical rules derived from domain expertise, while the Fuzzy Logic System utilizes fuzzy membership functions and inference rules to handle uncertainty and imprecise medical information. The performance of both models is evaluated using Accuracy, Precision, Recall, and F1-Score metrics. Experimental results indicate that the Fuzzy Logic Inference System outperforms the Rule-Based Expert System by providing more reliable predictions in the presence of uncertain and ambiguous clinical data. The findings highlight the effectiveness of fuzzy logic-based decision support systems in improving diagnostic accuracy and assisting healthcare professionals in early heart disease detection and treatment planning.},
        keywords = {Artificial Intelligence, Heart Disease Prediction, Rule-Based Expert System, Fuzzy Logic Inference System, Clinical Decision Support System, Healthcare Analytics, Medical Diagnosis, UCI Heart Disease Dataset, Disease Prediction},
        month = {June},
        }