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Optimization of RFM for automated breast cancer detection

Recent years have seen an upsurge in the acceptance of illness diagnosis and prediction utilizing ML algorithms. A ML model can be employed in the diagnosis of breast cancer illness. In this research, an effective breast cancer prediction model with grid search approach is provided. Using the random forest approach, grid search is used to find the best n-estimator, which may provide the highest possible accuracy for predicting breast cancer. The accuracy of the suggested model can then be utilised to contrast its effectiveness to that of a standard RFM. The experimental result analysis demonstrates that the optimized model has 97.07 percent accuracy whereas the regular random forest technique has an accuracy of 94.73 percent in breast cancer detection.