Abstract
Purpose. Although different cancer types have been investigated from the perspective of biomedical sciences, machine learning-based studies have been scant, particularly in addressing the temporal impact of brain and central nervous system (BCNS) cancer survival. The present study aims to ll this gap by proposing a machine learning methodology to investigate the temporal effects of the attributes and the levels at which they are associated with BCNS cancer survival. Methods. Following the best practices in health analytics, the proposed methodology utilizes a variety of feature selection, data balancing, and sensitivity analysis methods to optimize the knowledge discovery process and the resultant outcomes. Results. The ndings can potentially assist medical professionals in identifying and targeting specic subsets of features and levels of attributes associated with sharply decreasing (or increasing) survival rates; thereby implementing better treatment options to improve the survival chances of BCNS cancer patients. Conclusion. Although the proposed hybrid methodology is validated on a large and feature-rich BCNS cancer data set, it can be utilized to study survival prognostics of other cancer or chronic disease types.