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

Timothy Samec, Raed Seetan


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 of gynecological malignancies, has no effective screening with over 70% of patients being diagnosed in an advanced stage. The aim of this study is to determine the most statistically significant contributing factors through a multivariate regression into the severity of female gynecological cancers. Data from the SEER cancer database were utilized in this study. Several attempted multivariate linear regressions were implemented with further reduced models; however, a linear model could not be properly fit to the data. Because of unmet assumptions, nonparametric moving, local regression, LOESS, was performed. After smoothing factors were included to reduced models, residual information was minimized although few conclusions can be drawn from the resulting statistics. These issues were prevalent mainly because of the massive variability in the data and inherent lack of linearity. This can be a significant issue with clinical data that does not dive deeper into cancer-dependent factors including genetic expression and cell surface receptor overexpression. General patient demographic data and diagnostic information alone does not provide enough detail to make a definite conclusion or prediction on patient survivability.



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