Breast Cancer Recurrence and Mortality Risk Data for 427 Women with Pesticide Exposure

427 women's clinicopathological data were used to train machine learning models for predicting breast cancer recurrence and mortality. The study, authored by Isabella Cristina Cazagranda, demonstrates a 24.12% improvement in model prediction quality when pesticide exposure is included as a risk factor. The dataset was last updated on June 3, 2026.

Use Cases

• Training risk stratification models based on clinicopathological data and pesticide exposure history.
• Comparing machine learning algorithm performance (e.g., random forest, logistic regression) for predicting cancer outcomes.
• Investigating the impact of occupational pesticide exposure on breast cancer recurrence and mortality rates.

Strengths

• Includes data from 427 women, providing a basis for model training.
• Model performance was evaluated using precision, accuracy, recall, F1-score, and AUC-ROC metrics.
• Results show a specific 24.12% improvement in prediction quality for the best model when pesticide exposure is considered.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is small in scale at 357.5 KB, indicating limited scope.

Provenance

Source

Isabella Cristina Cazagranda

Collection Method

Clinicopathological data collected from patients, analyzed with machine learning algorithms including logistic regression, random forest, support vector machine, and gradient boosting.

Freshness

Last updated 2026-06-03 04:47:08; freshness should be verified.

Geography

Likely Brazil, as the description references it as the world's largest consumer of pesticides and a region of family farming.