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

## TL;DR

This paper introduces a dataset and preprocessing method for predicting breast cancer risk using data from Cuban women, supporting machine learning in public health.

## Contribution

A reproducible data preparation method for breast cancer risk prediction tailored to a Cuban population.

## Key findings

- A dataset of 1697 Cuban women's breast cancer risk factors was collected and processed.
- The preprocessing method ensured data quality and compatibility with machine learning techniques.
- Prediction models showed consistent performance across multiple metrics after preprocessing.

## Abstract

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

Image, graphical abstract