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Evaluates random forest model performance in breast cancer detection, suggesting improved accuracy.
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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Rida Mohammed (2026) studied this question.
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