Breast Cancer Classification Using Naïve Bayes and Random Forest Algorithms

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This study shows Random Forest improves classification accuracy in breast cancer detection, indicating its benefit over Naïve Bayes.

Random Forest achieved an accuracy of 99.27% in classifying breast cancer, outperforming Naïve Bayes, which had 83.78%.
Precision and recall for Random Forest were at 99.30% and 99.27%, respectively, indicating strong prediction capabilities.
The study used a dataset from Kaggle, analyzed with RapidMiner, splitting data into 80% training and 20% testing.
Findings suggest Random Forest's ensemble approach can handle complexity better, making it suitable for medical decision support systems.

Gurning et al. (2025) studied this question.

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View Full Paper

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Authors

This study shows Random Forest improves classification accuracy in breast cancer detection, indicating its benefit over Naïve Bayes.

Random Forest achieved an accuracy of 99.27% in classifying breast cancer, outperforming Naïve Bayes, which had 83.78%.
Precision and recall for Random Forest were at 99.30% and 99.27%, respectively, indicating strong prediction capabilities.
The study used a dataset from Kaggle, analyzed with RapidMiner, splitting data into 80% training and 20% testing.
Findings suggest Random Forest's ensemble approach can handle complexity better, making it suitable for medical decision support systems.

Gurning et al. (2025) studied this question.

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