Utilizing patient data: A tutorial on predicting second cancer with machine learning models.

Background: The article explores the potential risk of secondary cancer (SC) due to radiation therapy (RT) and highlights the necessity for new modeling techniques to mitigate this risk.

Methods: By employing machine learning (ML) models, specifically decision trees, in the research process, a practical framework is established for forecasting the occurrence of SC using patient data.

Results & Discussion: This framework aids in categorizing patients into high-risk or low-risk groups, thereby enabling personalized treatment plans and interventions.