Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms.

ABSTRACT: Metastatic Breast Cancer (MBC) is one of the primary causes of cancer-related deaths in women. Despite several limitations, histopathological information about the malignancy is used for the classification of cancer. The objective of our study is to develop a non-invasive breast cancer classification system for the diagnosis of cancer metastases. The anaconda-Jupyter notebook is used to develop various python programming modules for text mining, data processing, and Machine Learning (ML) methods. Utilizing classification model cross-validation criteria, including accuracy, AUC, and ROC, the prediction performance of the ML models is assessed. Welch Unpaired t-test was used to ascertain the statistical significance of the datasets. Text mining framework from the Electronic Medical Records (

SUBMITTER: Botlagunta M 

PROVIDER: S-EPMC9831019 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature