Several studies have successfully applied gradient boosting in cancer research:
Early Detection: Gradient boosting algorithms have been used to develop predictive models for early detection of cancers such as breast and lung cancer. Prognosis: By analyzing patient data, these models can predict survival rates and disease recurrence, aiding in better prognosis. Personalized Treatment: Gradient boosting helps in identifying the most effective treatment plans based on individual patient profiles.
