Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings

International Journal of Emergency Medicine

Volumn 2, Issue 2, 2009, Pages 99-105

  Eken, Cenker ^a   Bilge, Ugur ^a   Kartal, Mutlu ^a   Eray, Oktay ^a  

^a AKDENIZ UNIVERSITY   (Turkey)

Author keywords

Artificial neural network; Emergency department; Genetic algorithm; Logistic regression; Renal colic

Indexed keywords

ADULT; AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACKACHE; CLINICAL DECISION MAKING; CONTROLLED STUDY; DATA MINING; DILATATION; ECHOGRAPHY; EMERGENCY CARE; EMERGENCY WARD; FEMALE; FEVER; FLANK PAIN; GENETIC ALGORITHM; HUMAN; INTERMETHOD COMPARISON; KIDNEY COLIC; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; MAXIMUM LIKELIHOOD METHOD; PELVICALICEAL DILATATION; PREDICTION; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; UROLITHIASIS;

EID: 77953479391     PISSN: 18651372     EISSN: 18651380     Source Type: Journal    
DOI: 10.1007/s12245-009-0103-1     Document Type: Article

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