Cervical Cancer Risk Prediction Using Machine Learning

Overview

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In this hands-on project, we will build and train an XG-Boost classifier to predict whether a person has a risk of having cervical cancer. Cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. Data has been obtained from 858 patients and include features such as number of pregnancies, smoking habits, Sexually Transmitted Disease (STD), demographics, and historic medical records.

Syllabus

Cervical Cancer Risk Prediction Using Machine Learning

In this hands-on project, we will build and train an XG-Boost classifier to predict whether a person has a risk of having cervical cancer. Cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. Data has been obtained from 858 patients and include features such as number of pregnancies, smoking habits, Sexually Transmitted Disease (STD), demographics, and historic medical records. Results have shown that High sexual activity Human papilloma virus (HPV) is one of the key factors that increases the risk of having cervical cancer. The presence of hormones in oral contraceptives, having many children, and smoking increase the risk for developing cervical cancer, particularly in women infected with HPV. Also, people with weak immune systems (HIV/AIDS) have high risk of HPV.