Model Analysis for Predicting Prostate Cancer Patient’s Survival: A SEER Case Study

Abstract

Prostate cancer is assumed to be the most familiar cancer and the principal cause of death in the world. For effective treatment to decrease mortality, an accurate survival projection is essential. A remedy plan can be scheme under the predicted survival state. Machine Learning (ML) approaches have recently attracted significant attention, particularly in constructing data-driven prediction models. Prostate cancer survival prediction has received little attention in research. In this article, we constructed models with the support of ML techniques to determine the possibility of whether a patient with prostate cancer will survive or not. Feature impact analysis, a good amount of data, and a distinctive track make our model’s results better compared to previous research. The models have created using data from the SEER (Surveillance, Epidemiology, End Results) database. SEER program collects and distributes cancer statistics to lessen the disease impact. Using around twelve prediction models, we assessed the survival of prostate cancer patients. HGB, LGBM, XGBoost, Gradient Boosting, and Ada Boost are notable prediction models. Among them, the XGBoost is the best contribution, with an accuracy of 89.57%, and found to be faster among the models.