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Retinoblastoma is a childhood cancer grows in retina. Although it could be treated in early stages, it can spread to nervous system and also other parts of the body and eventually may cause death in this situation. The prediction of survivability attracts a considerable interest and has been studied at different types of cancers, like breast, lung, colon and thyroid in literature by applying data mining methods. Data used in this study is obtained from The Surveillance, Epidemiology, and End Results (SEER) program which is an authorized data repository of cancer statistics. In our study, the survivability for retinoblastoma is predicted on SEER dataset consisting of 1258 patients by using data mining algorithms (support vector machines, logistic regression, multi-layer perceptron, naïve bayes, random forest and decision trees). Two strategies for imbalanced data which are over-sampling (synthetic minority over-sampling - SMOTE) and under-sampling are used. Results are analyzed and compared with the ones studied in other cancer types.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Gülistan Özdemir Özdoğan
Hilal Kaya
Baha Şen
I. CANKAYA