Nath, GopalCoursey, AustinEkong, JosephRastegari, ElhamSengupta, SaptarshiDag, Asli Z.Delen, Dursun2024-05-192024-05-1920232047-97002047-9719https://doi.org10.1080/20479700.2023.2196101https://hdl.handle.net/20.500.12713/5630Purpose Although different cancer types have been investigated from the perspective of biomedical sciences, machine learning-based studies have been scant. The present study aims to uncover the temporal effects of factors that are important for brain and central nervous system (BCNS) cancer survival, by proposing a machine learning methodology. Methods Several feature selection, data balancing, and machine learning algorithms (in addition to the sensitivity analysis) were employed to analyze the dynamic (i.e. varying) effects of several feature sets on the survival outputs. Results The results show that Gradient Boosting (GB) along with Logistic Regression (LR) and Artificial Neural Networks (ANN) outperform the other classification algorithms in this study. Furthermore, it has been observed that the importance of several features/variables varies from 1- to 5- and 10-year survival predictions. Conclusion Although the proposed hybrid methodology is validated on a large and feature-rich BCNS cancer data set, it can also be utilized to study survival prognostics of other cancer or chronic disease types.eninfo:eu-repo/semantics/openAccessTemporal EffectMachine LearningHealthcare AnalyticsParsimonious ModelBrain And Other Nervous System CancerPredictive ModelingArticleWOS:0009623484000012-s2.0-85151487062N/A10.1080/20479700.2023.2196101Q2