Genetic algorithm pruning of probabilistic neural networks in medical disease estimation

Neural Networks

Volumn 24, Issue 8, 2011, Pages 831-835

  Mantzaris, Dimitrios ^a   Anastassopoulos, George ^b,c   Adamopoulos, Adam ^b,c  

^a TECHNOLOGICAL EDUCATIONAL INSTITUTE OF KAVALA   (Greece)

^b DEMOCRITUS UNIVERSITY OF THRACE   (Greece)

^c HELLENIC OPEN UNIVERSITY   (Greece)

Author keywords

Feature selection; Genetic algorithms; Probabilistic neural networks pruning; Vesicoureteral reflux

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLASSIFICATION ACCURACY; DATA RECORDS; DIAGNOSIS PROCEDURE; HIDDEN LAYERS; HYBRID MODEL; PATTERN CLASSIFICATION PROBLEMS; PROBABILISTIC NEURAL NETWORK MODELS; PROBABILISTIC NEURAL NETWORKS; RISK FACTORS; TESTING PHASE; TRAINING PHASE; VESICOURETERAL REFLUX;

DIAGNOSIS; FEATURE EXTRACTION; GENETIC ALGORITHMS; PROBABILISTIC LOGICS; RISK PERCEPTION;

NEURAL NETWORKS;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; DIAGNOSTIC PROCEDURE; ERROR; GENETIC ALGORITHM; HYBRID; PREDICTION; PRIORITY JOURNAL; PROBABILITY; PROGNOSIS; RISK ASSESSMENT; RISK FACTOR;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CHILD, PRESCHOOL; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; GENETICS; HUMANS; INFANT; KIDNEY; KIDNEY PELVIS; MALE; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PROGNOSIS; REPRODUCIBILITY OF RESULTS; RISK FACTORS; URINARY TRACT; URINARY TRACT INFECTIONS; URINE; URODYNAMICS; VESICO-URETERAL REFLUX;

EID: 80051786833     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2011.06.003     Document Type: Article

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