Modeling and analyzing predictive monthly survival in females diagnosed with gynecological cancers

Cancer ranks as a leading cause of death worldwide; an estimated 1.7 million new diagnoses were reported in 2021. Ovarian cancer, the most lethal gynecological malignancies, has no effective screening with over 70% patients being diagnosed advanced stage. The aim this study was to determine statistically significant contributing factors through multivariate regression into severity female cancers. Data from surveillance, epidemiology, and end results program (SEER) cancer database utilized study. Several attempted linear regressions implemented further reduced models; however, model could not be properly fit data. Because unmet assumptions, nonparametric moving, local regression, locally scatterplot smoothing (LOESS), performed. After included reduced-models, residual information minimized although few conclusions can drawn resulting statistics. These issues prevalent mainly because massive variability data inherent lack linearity. This issue clinical that does dive deeper cancer-dependent including genetic expression cell surface receptor overexpression. General patient demographic diagnostic alone provide enough detail make definite conclusion or prediction on survivability. Increased attention acquisition tumor tissue for genomic proteomic analysis addition next-generation sequencing methods lead improvements prognostic predictions.