Symbiotic evolution-based design of fuzzy-neural diagnostic system for common acute abdominal pain
This paper presents a symbiotic evolution-based fuzzy-neural diagnostic system (SE-FNAAPDS) for diagnosis of common acute abdominal pain (AAP) without professional medical examination. The computer-assisted diagnostic system is formatted a multiple-choice symptom questionnaire, with a prompt/help menu to assist a layman user in obtaining accurate symptom data using nothing more technologically sophisticated than a medical-type thermometer and stethoscope. The SE-FNAAPDS combination of fuzzy modeling, back-propagation training and symbiotic evolution function auto-generates its own optimal fuzzy neural architecture, a significant advantage over previous time-consuming manual parameter determination. A set of AAP clinical feature data and confirmed diagnostic results is applied as input/output rule-generation and training data for the fuzzy-neural network. Comparison of system construction time and diagnostic accuracy is made by applying the same database to SE-FNAAPDS and three traditional systems. Compared to traditional methods, diagnostic decisions from SE-FNAAPDS show 94% agreement with professional human medical diagnosis and less CPU time for system construction. The presented design is useful as a core module for more advanced computer-assisted diagnostic systems, and for direct application in AAP diagnosis.
symbiotic evolution acute abdominal pain fuzzy modeling back-propagation neural network computer-assisted medical diagnosis