A hypertext knowledge based for primary care - LIMEDS in LINCKS
SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
Strategies for efficient incremental nearest neighbor search
Pattern Recognition
Processing unexact information in a medical used multiparadigm system
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Fuzzy Temporal/Categorical Information in Diagnosis
Journal of Intelligent Information Systems - Special issue on integrating artificial intelligene and database technologies
Towards More Optimal Medical Diagnosing with Evolutionary Algorithms
Journal of Medical Systems
A Neural Network Based Model for Prognosis of Early Breast Cancer
Applied Intelligence
Soft computing system for bank performance prediction
Applied Soft Computing
A robust neuro-fuzzy network approach to impulse noise filtering for color images
Applied Soft Computing
Computer Methods and Programs in Biomedicine
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Every approach to handling automation has its unique limitations. In the symbolic (rule base) approach, the brittleness of rules leads to the ineffectiveness of handling noisy data, but it derives its strengths in heuristic search. In the same vein, a case base reasoning paradigm is bedeviled with retrieval and adaptation problems. Neural Networks (NN) methodology suffers from intolerance of incremental insertion of new knowledge and limited explanation capability, but triumphs over other methods when it comes to adaptation using its generalization characteristics. Based on all these, a tight coupling of case base, rule base and neural networks methodologies is proposed for medical diagnosis. The case base provides the 'desired' outputs, which constitute an input to the neural networks. The results obtained from the trained neural networks assisted in formulating diagnostic rules, which form the rule base. Through the rule base, an inference engine that represents the hybrid is built. Data collected from three hospitals in Nigeria on hepatitis patients were used to test the functionality of the proposed system. The results obtained from the hybrid were compared with that obtained from the Multilayer Peceptron Neural Networks (MLPNN) training using NeuroSolutions 5.0 and found to covary strongly. The hybrid exhibits an explanation characteristic, a feature not found in neural networks.