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has been cited by the following article:

• TITLE: Decipher Clinical and Genetic Underpins of Breast Cancer Survival with Machine Learning Methods

  AUTHORS: Zhengkai Zhuang

  KEYWORDS: Machine Learning, Breast Cancer Prediction, Data Analysis, Feature Importance Comparison

  JOURNAL NAME: Advances in Breast Cancer Research, Vol.12 No.4, October 26, 2023

  ABSTRACT: Breast cancer is one of the most common cancers among women in the world, with more than two million new cases of breast cancer every year. This disease is associated with numerous clinical and genetic characteristics. In recent years, machine learning technology has been increasingly applied to the medical field, including predicting the risk of malignant tumors such as breast cancer. Based on clinical and targeted sequencing data of 1980 primary breast cancer samples, this article aimed to analyze these data and predict living conditions after breast cancer. After data engineering, feature selection, and comparison of machine learning methods, the light gradient boosting machine model was found the best with hyperparameter tuning (precision = 0.818, recall = 0.816, f1 score = 0.817, roc-auc = 0.867). And the top 5 determinants were clinical features age at diagnosis, Nottingham Prognostic Index, cohort and genetic features rheb, nr3c1. The study shed light on rational allocation of medical resources and provided insights to early prevention, diagnosis and treatment of breast cancer with the identified risk clinical and genetic factors.