A Fuzzy Logic Expert System for the Early Diagnosis of Cardiovascular Disorders

International Journal of Computer Science and Technology | Vol. 7, No. 12, December 2005 | pp. 89–96

DOI: 10.46882/2005/IJCST/071215

Research Article

Title: A Fuzzy Logic Expert System for the Early Diagnosis of Cardiovascular Disorders

Names of Authors: Santiago Ortiz¹, Nneka Okoye²

Authors’ Affiliations: ¹Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires, Argentina; ²Department of Computer Science, University of Ibadan, Ibadan, Nigeria

Abstract:
Developing clinical decision support systems is essential to aid medical practitioners in interpreting complex, multi-variate diagnostic profiles. This study introduces an automated clinical expert system based on fuzzy logic principles for the early risk stratification of cardiovascular disorders. The inference engine processes eleven physiological inputs, including blood pressure readings, serum cholesterol levels, maximum heart rate, and electrocardiogram (ECG) ST-segment depressions. Membership functions map these raw values into linguistic variables, which are then parsed through a rule base of 45 expert-validated conditions. Testing was conducted on a clinical dataset of 303 patient profiles. The fuzzy system achieved a diagnostic sensitivity of 92.4% and a specificity index of 89.1%. Defuzzification tasks, performed using the centroid method, yielded a crisp risk percentage within t = 8 ms. This fast processing time confirms that the system can be deployed on low-cost computing terminals in rural health centers.

Keywords: Fuzzy Logic, Expert Systems, Cardiovascular Diagnosis, Decision Support Systems, Medical Informatics

Manuscript Timeline: Received September 04, 2005; Revised October 20, 2005; Accepted November 11, 2005; Published December 27, 2005

Citation: Ortiz, S., & Okoye, N. (2005). A Fuzzy Logic Expert System for the Early Diagnosis of Cardiovascular Disorders. International Journal of Computer Science and Technology, 7(12), 89–96.

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