Early Stage Ovarian Cancer Prediction using Machine Learning

Abstract

The most dangerous cancer that affects women is ovarian cancer and the early-stage diagnosis is difficult. To overcome this issue, machine learning techniques being used to predict the early stage of ovarian cancer in women. This paper discusses the different features that link with the prediction of cancer through clinical data. The different machine learning algorithms, like logistic regression, support vector machines (SVM), and decision trees are the primary focus of the paper to predict cancer at the early stage. This paper focuses on the accuracy of different models like logistic regression, support vector machine, decision tree used to predict the early stage cancer. The paper discusses an integrated approach that uses random forest feature selection method and a random forest classifier to give more accurate results. The proposed model has accuracy of 91% as compared to the other models with accuracy 81%,84%,83% respectively.