Random Forest-Based Approach for Integrating Blood Profile in Metastatic Breast Cancer Classification

This study employs a random forest classifier to analyze a novel blood profile dataset for predicting metastatic breast cancer. The results demonstrate high performance, with an accuracy of 98%, precision of 97%, recall of 98%, and F1-score of 96%. The utilization of blood profile data in cancer prediction represents a novel approach, showcasing the potential of integrating diverse datasets for enhanced diagnostic accuracy. This research contributes to advancing the field of oncology by showcasing the effectiveness of incorporating comprehensive blood profiles into predictive models, paving the way for improved cancer detection and treatment strategies.