Ovarian Cancer Detection and Classification Using Machine Leaning
Ms Aditya, I. Amrita, Ashwini Kodipalli, Roshan Joy Martis
Abstract
Ovarian cancer is one of the leading causes of death among women. It ranks fifth in cancer deaths among women and affects women if all demography and ethnicity. It is important to accurately classify it from tumours hence avoiding false positives for cancer, and hence catered to the patients appropriate needs. In this direction a methodology is designed to classify between Benign Ovarian Tumourand Ovarian Cancer with different machine learning classifiers with different imputation methods, with and without feature selection and deep learning from Kaggle dataset. It was evident that feature selection greatly increased the performance of the Machine learning model through accuracy. Out of all the classifiers Random Forest with median imputation gave the best result. The accuracy of DL model was on-par with the Random Forest classifier and did not show any significant improvement over the traditional machine learning model.