Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application
Sami Akbulut, İpek Balıkçı Çiçek, Cemil Çolak
Abstract
The diagnosis of breast cancer can be accomplished using an algorithm or an early detection model of breast cancer risk via determining factors. In the present study, gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) models were applied and their performances were compared.
Topics & Concepts
Boosting (machine learning)InformaticsBreast cancerPublic healthHealth informaticsComputer scienceMedicineArtificial intelligenceCancerEngineeringInternal medicineNursingElectrical engineeringArtificial Intelligence in HealthcareAI in cancer detection