Data preparation method for machine learning-based breast cancer risk prediction: A Cuban case study

This article presents a dataset of breast cancer risk factors collected from 1697 Cuban women between 2001 and 2018, as a tool to design and support the development and validation of predictive models in public health for breast cancer risk. A reproducible methodology for quality control and variable enrichment was implemented to ensure data integrity and compatibility with machine learning techniques. • Reproducible preprocessing methodology to ensure data quality and traceability. • Open breast cancer risk factor dataset for epidemiological studies and risk assessment using machine learning. • Consistent prediction model performance across multiple metrics after data preprocessing.