Breast Cancer Classification using Random Forest Algorithm

Abstract

This study uses detect breast cancer based on Random Forest (RF). It is crucial to diagnose the illness to identify treatment solutions closely linked to patient safety. Breast cancer is diagnosed using past medical records and various classification methods used in data mining fields today. Each technique performs differently depending on the input feature types and model parameters. Neutral Networks have been proven to be more effective in data analysis and pre-diagnosis without requiring medical knowledge. The study reduces diagnostic variance and increases diagnostic accuracy by overcoming the limitation of individual models. The Random Forest model had a training and validation accuracy of 90% and 91%.