Predictive Analytics for Breast Cancer Survival Rates Using Random Forest

The Random Forest algorithm was used in this study to build a prediction model for breast cancer survival rates. The study used a deductive methodology, a descriptive design, including secondary data collecting in accordance with the interpretivism philosophy. The dataset was painstakingly preprocessed, and numerous metrics were used to gauge the model's effectiveness. The significance of the model's features was investigated as well to offer insight into the factors affecting survival forecasts. The study emphasizes how the model's practical applications might improve patient care by guiding treatment choices. It assesses the model's resilience under various circumstances, admits its shortcomings, and suggests future study trajectories. The study demonstrates the way predictive models have the ability to greatly affect patient outcomes and breast cancer care.