Prediction of Living Status for Breast Cancer Patients Using Machine Learning

Yaqian Han

doi:10.54097/rnqpr864

Authors

Yaqian Han

DOI:

https://doi.org/10.54097/rnqpr864

Keywords:

Breast cancer, Machine learning, Random Forest, Recursive feature elimination.

Abstract

Due to reasons such as diet, living habits, and genes, etc., breast cancer has gradually become a common high mortality disease and is not limited to women. Many patients suffer from terrible illness or even death due to untimely treatment or incorrect prediction of tumors. This study has 500 observations in total through utilizing the combination of oversampling and undersampling. What’s more, by using advanced machine learning, the method of recursive feature elimination and random forest, this research constructs a model and select four types of protein content in the body and age as the most relevant features in predicting patients’ living status. The model has a relative high accuracy 98.052% and can provide physicians relative accurate information to give targeted treatment. It also can be utilized as a warning to remind patients of their current physical condition. Early treatment and accurate diagnosis can significantly improve patient survival rates.

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