Ovarian cancer is characterised by the greatest mortality among all tumors of the reproductive tract. This study included 246 patients which consisted of 136 women with ovarian cancer without genetic mutation and 110 women with benign ovarian cysts. We created two mathematical logic models containing positive and negative risk factors of ovarian cancer such as: age at last menstruation cycle, patient age, OC, HRT, smoking, education status, and alcohol consumption. The calculated cut-off point for the first model was 0.5117. Classification determined on the basis of that cut-off point yielded 87.19% of correctly classified cases, of which 91.38% are "case" and 81.61% - "noncase". For the second model the designated cut-off point was set at 0.5149 and the percentage of correctly classified patients was 88.12%, with 92.24% correctly rated as cancer patients and 82.56% of the cases rightly recognised as having no ovarian cancer. Logit is a simple mathematical model that can be a useful tool for identification of patients with increased risk of ovarian cancer.
Keywords: Logit; Mathematical model; Ovarian cancer; Risk factor.