Identifying biomarkers that predict patient’s risk for Ovarian Cancer is a key factor in the fight to improve survival rates. Ovarian Cancer is a group of diseases that originate in the ovaries, fallopian tubes or peritoneum. Ovarian Cancer is best treated at its earliest stages when it is most treatable. Therefore, early screening and diagnosis is key to successfully treating or curing the disease. This study will use heatmap visualization, pearson correlation coefficient method, scatterplot visualizations, logistic regression, and existing literature to determine the best biomarkers of importance in comparison with elevated CA125 levels importance identified include Age, Menopause, Human Epididymis Protein 4 (HE4), Alkaline Phosphatase (ALP), and Calcium. Preliminary analysis shows variables of interest, except HE4, correspond with elevated CA125 levels and would be biomarkers to play closer attention to in predicting ovarian cancer with machine learning models. To optimize performance of the prediction model, removal of non-biomarkers, Age and Menopause, is necessary. Menopause is a nominal category that could still decrease performance even if its cleaned and converted to numeric form.