title: Comparative study of intelligent models for the prediction of bladder cancer progression.
abstract: New techniques for the prediction of tumour behaviour are needed since statistical analysis has low accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide suitable methods. We have compared the predictive accuracies of neuro-fuzzy modelling (NFM), artificial neural networks (ANN) and traditional statistical methods for the prediction of bladder cancer. Experimental molecular biomarkers, including p53 expression and gene methylation, and conventional clinicopathological data were studied in a cohort of 117 patients with bladder cancer. For all 3 methods, models were produced to predict the presence and timing of tumour progression. Both methods of AI predicted progression with an accuracy ranging from 88-100%, which was superior to logistic regression, and NFM appeared to be better than ANN at predicting the timing of progression.
authors.affiliation: Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK. m.f.abbod@sheffield.ac.uk
authors.forename: M F
authors.initials: MF
authors.lastname: Abbod
authors.forename: D A
authors.initials: DA
authors.lastname: Linkens
authors.forename: J W F
authors.initials: JW
authors.lastname: Catto
authors.forename: F C
authors.initials: FC
authors.lastname: Hamdy
mesh_terms: D014408:Biomarkers, Tumor / Q000032:analysis; D002295:Carcinoma, Transitional Cell / Q000235:genetics / Q000473:pathology*; D015331:Cohort Studies; D018450:Disease Progression; D017143:Fuzzy Logic; D020869:Gene Expression Profiling; D016158:Genes, p53; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011379:Prognosis; D001749:Urinary Bladder Neoplasms / Q000235:genetics / Q000473:pathology*
chemical_list: D014408:Biomarkers, Tumor