Artificial intelligence for the prediction bladder cancer
New techniques for the prediction of tumour behaviour are needed as statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. We have previously shown that the predictive accuracies of neuro-fuzzy modelling (NFM...
| Main Authors: | , , , , |
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| פורמט: | Journal article |
| שפה: | English |
| יצא לאור: |
2004
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| _version_ | 1826275843270246400 |
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| author | Abbod, M Catto, J Chen, M Linkens, D Hamdy, F |
| author_facet | Abbod, M Catto, J Chen, M Linkens, D Hamdy, F |
| author_sort | Abbod, M |
| collection | OXFORD |
| description | New techniques for the prediction of tumour behaviour are needed as statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. We have previously shown that the predictive accuracies of neuro-fuzzy modelling (NFM) and artificial neural networks (ANN), two methods of AI, are superior to traditional statistical methods for the behaviour of bladder cancer (Catto et al, 2003). In this paper, we explain the AI techniques required to produce these predictive models. We used 9 parameters, which were a combination of experimental molecular biomarkers and conventional clinicopathological data, to predict the risk of tumour progression in a population of 109 patients with bladder cancer. NFM, using fuzzy logic to model data, achieved similar or superior predictive accuracy to ANN, which required cross-validation. However, unlike the impenetrable opaque structure of neural networks, the rules of NFM are transparent, enabling validation from clinical knowledge and the manipulation of input variables to allow exploratory predictions. |
| first_indexed | 2024-03-06T23:04:58Z |
| format | Journal article |
| id | oxford-uuid:63750c4d-6500-4e1c-8b51-23cf3b5ab6ad |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:04:58Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | oxford-uuid:63750c4d-6500-4e1c-8b51-23cf3b5ab6ad2022-03-26T18:13:11ZArtificial intelligence for the prediction bladder cancerJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:63750c4d-6500-4e1c-8b51-23cf3b5ab6adEnglishSymplectic Elements at Oxford2004Abbod, MCatto, JChen, MLinkens, DHamdy, FNew techniques for the prediction of tumour behaviour are needed as statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. We have previously shown that the predictive accuracies of neuro-fuzzy modelling (NFM) and artificial neural networks (ANN), two methods of AI, are superior to traditional statistical methods for the behaviour of bladder cancer (Catto et al, 2003). In this paper, we explain the AI techniques required to produce these predictive models. We used 9 parameters, which were a combination of experimental molecular biomarkers and conventional clinicopathological data, to predict the risk of tumour progression in a population of 109 patients with bladder cancer. NFM, using fuzzy logic to model data, achieved similar or superior predictive accuracy to ANN, which required cross-validation. However, unlike the impenetrable opaque structure of neural networks, the rules of NFM are transparent, enabling validation from clinical knowledge and the manipulation of input variables to allow exploratory predictions. |
| spellingShingle | Abbod, M Catto, J Chen, M Linkens, D Hamdy, F Artificial intelligence for the prediction bladder cancer |
| title | Artificial intelligence for the prediction bladder cancer |
| title_full | Artificial intelligence for the prediction bladder cancer |
| title_fullStr | Artificial intelligence for the prediction bladder cancer |
| title_full_unstemmed | Artificial intelligence for the prediction bladder cancer |
| title_short | Artificial intelligence for the prediction bladder cancer |
| title_sort | artificial intelligence for the prediction bladder cancer |
| work_keys_str_mv | AT abbodm artificialintelligenceforthepredictionbladdercancer AT cattoj artificialintelligenceforthepredictionbladdercancer AT chenm artificialintelligenceforthepredictionbladdercancer AT linkensd artificialintelligenceforthepredictionbladdercancer AT hamdyf artificialintelligenceforthepredictionbladdercancer |