Cervical Cancer Risk Prediction Using Machine Learning
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About this course
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.
What you'll learn
- understand the application of machine learning in healthcare
- build and train an XG-Boost classifier
- analyze patient data to predict health risks
Course objectives
- apply machine learning techniques to real-world health data
- improve predictive modeling skills relevant to public health
Skills you'll gain
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