A Resampling Univariate Analysis Approach to Ovarian Cancer From Clinical and Genetic Data

Ovarian cancer (OC) is the second most common gynecological malignancy and tumor with worst prognosis. To try to improve this situation, Data Science technologies could be a useful tool help clinicians know more about disease. In our case, we are interested in exploring OC data discover relationships between clinical genetic factors disease progression. For it, propose an analysis framework for simple univariate statistical descriptions of features different types, based on bootstrap resampling. Foremost, define metric, categorical, dates variables determine what advantages disadvantages using resampling strategies, their basis. Then, use it perform over dataset that allows explore how progression, having platinum-free interval as indicator, relation types. Also, provides first set possibly relevant survival prediction. Results obtained show some have led individual differences both platinum resistant (<; 6 months) sensitive(>6 groups. It can concluded indicator database discriminatory hypotheses studied, though convenient make multivariate analyses check among influenced.