Artificial intelligence in predicting bladder cancer outcome: A comparison of neuro-fuzzy modeling and artificial neural networks

Clinical Cancer Research

Volumn 9, Issue 11, 2003, Pages 4172-4177

  Catto, James W F ^a   Linkens, Derek A ^a   Abbod, Maysam F ^a   Chen, Minyou ^a   Burton, Julian L ^a   Feeley, Kenneth M ^b   Hamdy, Freddie C ^a,b  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

^b ROYAL HALLAMSHIRE HOSPITAL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN P53;

ACCURACY; ADULT; AGED; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BLADDER CARCINOMA; CANCER GRADING; CANCER RECURRENCE; CLINICAL FEATURE; COMPARATIVE STUDY; CONTROLLED STUDY; FEMALE; HUMAN; HUMAN TISSUE; MAJOR CLINICAL STUDY; MALE; MISMATCH REPAIR; MODEL; MOLECULAR MECHANICS; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; PROTEIN EXPRESSION; RELIABILITY; STATISTICAL ANALYSIS; VALIDATION PROCESS;

ARTIFICIAL INTELLIGENCE; BASE PAIR MISMATCH; CARCINOMA, TRANSITIONAL CELL; FUZZY LOGIC; HUMANS; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS; RECURRENCE; RISK FACTORS; SMOKING; TIME FACTORS; TREATMENT OUTCOME; URINARY BLADDER NEOPLASMS;

EID: 0141790049     PISSN: 10780432     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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