Name
Abstract | ||
|---|---|---|
New techniques for the prediction of tumour behaviour are needed since statistical analysis has a poor accuracy and is not applicable to the individual. Artificial Intelligence (AI) may provide these suitable methods. We have compared the predictive accuracies of neuro-fuzzy modelling (NFM), artificial neural networks (ANN) and traditional statistical methods, for the behaviour of bladder cancer. Experimental molecular biomarkers, including p53 expression and gene methylation, and conventional clinicopathological data were studied in a cohort of 122 patients with bladder cancer. For all 3 methods, models were produced to predict the presence and timing of a tumour progression. Both methods of AI predicted progression with an accuracy ranging from 88-100%. This was superior to logistic regression. NFM appeared better than ANN at predicting the timing of progression. |
Year | Venue | Keywords |
|---|---|---|
2005 | ESANN | artificial intelligent,artificial neural network,statistical analysis,neuro fuzzy,logistic regression |
Field | DocType | Citations |
Pattern recognition,Computer science,Bladder cancer,Biomarker (medicine),Artificial intelligence,Artificial neural network,Cohort,Logistic regression,Machine learning,Statistical analysis | Conference | 1 |
PageRank | References | Authors |
0.48 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maysam F. Abbod | 1 | 224 | 28.14 |
| Jim W. F. Catto | 2 | 1 | 1.16 |
| Min-you Chen | 3 | 274 | 22.18 |
| Derek A. Linkens | 4 | 215 | 25.36 |
| Freddie C. Hamdy | 5 | 3 | 1.26 |