Cervical Cancer Risk Prediction Model and Analysis of Risk Factors based on Machine Learning

Abstract

Cervical cancer, as one of the most common malignant tumor among women, is difficult to be diagnosed and studied due to its complexity of disease factors and challenged prediction. In this paper, a real data-driven powerful machine learning model is employed. With this technique, we model the detection methods of cervical cancer and determine the diagnostic accuracy of current mainstream methods for cervical cancer by multi-layer perceptron. Finally, the importance index of cervical cancer risk factors can be analyzed by random forest. The experiment results have shown that there is a close relationship between the risk factors and cervical cancer. And compared with other risk factors, age, number of sexual partners, hormonal contraceptives have a greater influence on the diagnosis of cervical cancer. Therefore, our research not only improves the predictability of cervical cancer risk, but also inspires the development of pathological model based on MLP and random forest.