What question did this study set out to answer?

This research aims to enhance breast cancer prediction models using a Random Forest classifier.

June 1, 2026Open Access

Extension of Breast Cancer Prediction Model using Random Forest

Puntos clave

This research aims to enhance breast cancer prediction models using a Random Forest classifier.
Introduced a Random Forest classifier to the existing breast cancer prediction dataset.
Applied preprocessing techniques and a 75–25 train-test split.
Implemented hyperparameter tuning, balanced class weights, and 5-fold cross-validation.
The Random Forest classifier demonstrated improved accuracy over the Decision Tree classifier reported previously (91.92% vs. 87.12%).
Hyperparameter tuning and class balancing contributed to better model performance.

Resumen

Breast cancer early detection can save lives, but accurate diagnosis remains challenging. Previous work showed that Support Vector Machine (SVM) outperformed a Decision Tree classifier (91.92% vs. 87.12% accuracy). This study extends the work by introducing a Random Forest classifier using the same dataset, preprocessing, and a 75–25 train-test split. Hyperparameter tuning, balanced class weights, and 5-fold cross-validation were applied.

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