Performance Comparison of CatBoost and Random Forest Algorithms for Breast Cancer Prediction: A Literature Review
M M. Baig, Payal P. Wankhede, Vaishnavi M. Samritkar, Prachi R. Meshram, Smita V. Raut, Purva Bhoyar, Bebi U. Thakre · 2023
Breast cancer is a major health concern that affects millions of women around the world, and early detection is crucial for improving patient outcomes. Machine learning algorithms have emerged as promising techniques for breast cancer prediction, with CatBoost and Random Forest among the most extensively used algorithms due to their capacity to handle big and complicated datasets, robustness to noisy data, and interpretability potential. However, the relative efficacy of these two algorithms in predicting breast cancer remains unknown. A Review of 20 relevant studies was conducted in this study. The objective of this study is to compare and evaluate the performance of CatBoost and Random Forest algorithms for breast cancer prediction. Our analysis took into account each algorithm's strengths and weaknesses, as well as the elements that may influence its performance. The strengths and weaknesses. of each algorithm were analysed and weaknesses, as well as the elements that may influence its performance. While both methods may produce accurate and reliable predictions, we discovered that their performance differs based on the dataset characteristics, features selection, and pre-processing procedures. Our evaluation sheds light on the relative efficacy of the CatBoost and Random Forest algorithms for breast cancer prediction. Furthermore, it may guide future research.