Survival prediction and treatment recommendation with Bayesian techniques in lung cancer.
In this paper, we investigate a number of Bayesian techniques for predicting 1-year- survival and making treatment selection recommendations for lung cancer. We have carried out two sets of experiments on the English Lung Cancer Dataset. For 1-year-survival prediction, the Naïve Bayes (NB) algorithm...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
2012
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| _version_ | 1797104892510208000 |
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| author | Sesen, M Kadir, T Alcantara, R Fox, J Brady, M |
| author_facet | Sesen, M Kadir, T Alcantara, R Fox, J Brady, M |
| author_sort | Sesen, M |
| collection | OXFORD |
| description | In this paper, we investigate a number of Bayesian techniques for predicting 1-year- survival and making treatment selection recommendations for lung cancer. We have carried out two sets of experiments on the English Lung Cancer Dataset. For 1-year-survival prediction, the Naïve Bayes (NB) algorithm achieved an area under the curve value of 81%, outperforming the Bayesian Networks learned by the M(3) and K2 structure learning algorithms. For treatment recommendation, the Bayesian Network, whose structure was learned by the MC(3) algorithm, has marginally outperformed NB, based on producing concordant results with the recorded treatments in the dataset. We observed that in cases where the classifier recommendations were discordant with the recorded treatments, the 1-year-survival rate decreased by 15%. We also observed that discordance between the classifier and the dataset was more dominant in cases where the recorded treatment was non-curative or was not frequently encountered in the dataset. |
| first_indexed | 2024-03-07T06:39:54Z |
| format | Journal article |
| id | oxford-uuid:f8e646d4-5ab1-4772-b811-adbd5a01648a |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T06:39:54Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oxford-uuid:f8e646d4-5ab1-4772-b811-adbd5a01648a2022-03-27T12:54:04ZSurvival prediction and treatment recommendation with Bayesian techniques in lung cancer.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f8e646d4-5ab1-4772-b811-adbd5a01648aEnglishSymplectic Elements at Oxford2012Sesen, MKadir, TAlcantara, RFox, JBrady, MIn this paper, we investigate a number of Bayesian techniques for predicting 1-year- survival and making treatment selection recommendations for lung cancer. We have carried out two sets of experiments on the English Lung Cancer Dataset. For 1-year-survival prediction, the Naïve Bayes (NB) algorithm achieved an area under the curve value of 81%, outperforming the Bayesian Networks learned by the M(3) and K2 structure learning algorithms. For treatment recommendation, the Bayesian Network, whose structure was learned by the MC(3) algorithm, has marginally outperformed NB, based on producing concordant results with the recorded treatments in the dataset. We observed that in cases where the classifier recommendations were discordant with the recorded treatments, the 1-year-survival rate decreased by 15%. We also observed that discordance between the classifier and the dataset was more dominant in cases where the recorded treatment was non-curative or was not frequently encountered in the dataset. |
| spellingShingle | Sesen, M Kadir, T Alcantara, R Fox, J Brady, M Survival prediction and treatment recommendation with Bayesian techniques in lung cancer. |
| title | Survival prediction and treatment recommendation with Bayesian techniques in lung cancer. |
| title_full | Survival prediction and treatment recommendation with Bayesian techniques in lung cancer. |
| title_fullStr | Survival prediction and treatment recommendation with Bayesian techniques in lung cancer. |
| title_full_unstemmed | Survival prediction and treatment recommendation with Bayesian techniques in lung cancer. |
| title_short | Survival prediction and treatment recommendation with Bayesian techniques in lung cancer. |
| title_sort | survival prediction and treatment recommendation with bayesian techniques in lung cancer |
| work_keys_str_mv | AT sesenm survivalpredictionandtreatmentrecommendationwithbayesiantechniquesinlungcancer AT kadirt survivalpredictionandtreatmentrecommendationwithbayesiantechniquesinlungcancer AT alcantarar survivalpredictionandtreatmentrecommendationwithbayesiantechniquesinlungcancer AT foxj survivalpredictionandtreatmentrecommendationwithbayesiantechniquesinlungcancer AT bradym survivalpredictionandtreatmentrecommendationwithbayesiantechniquesinlungcancer |