Application of Deep Learning-based Fuzzy Systems to Analyze the Overall Risk of Mortality in Glioblastoma Multiforme

Abstract

Glioblastoma multiforme (GBM) is the most aggressive brain cancer in adults, with 3.2-3.4 cases per 100 thousand. In the US, brain cancer does not rank in the top 10 causes of death, but it remains in the top 15. Therefore, this research proposes a fuzzy-based GRUCoxPH model to identify missense variants associated with a high risk of all-cause mortality in GBM. The study combines various models, including fuzzy logic, Gated Recurrent Units (GRUs), and Cox Proportional Hazards Regression (CoxPh), to identify potential risk factors. The dataset is derived from TCGA-GBM clinicopathological information and mutations to create four risk score models: GRU, CoxPH, GRUCoxPHAddition, and GRUCoxPHMultiplication, analyzing 9 risk factors of the dataset. The Fuzzy-based GRUCoxPH model achieves an average accuracy of 86.97%, outperforming other models. This model demonstrates its ability to classify and identify missense variants associated with mortality in GBM, potentially advancing cancer research.