Breast Cancer Detection Using Ensemble Classifiers for Accuracy Improvement - Mahboubeh Shamsi; Mohadaseh Karimian; Marziyeh Karimian (مدیریت مهندسی و رایانش نرم)
Early diagnosis of breast cancer plays a crucial role in treating the patient. Nowadays, data mining algorithms can provide intelligent methods in the health and treatment system that accurately detect breast cancer. The purpose of this study is breast cancer detection using ensemble classifier based on WBC and WDBC prepared databasesa. Our proposed model in the WBC database (reducing features by cfs+ optimizing samples using Resample+ ensemble classifier using data mining algorithms (kstar + random forest + Naïve Bayes and Bayes network)) has the best detection accuracy ( 100%), implementation time (0 seconds) and without any errors and on the WDBC database (reducing features by cfs+ optimizing samples using Resample+ ensemble classifier using data mining algorithms (IBK algorithm+ Naïve Bayes, Bayes network and kstar)) has an accuracy of 99/29, the implementation time is 0 seconds, and the mean absolute error is 0/007. The results of this study show that according to the ensemble classifier methods using data mining algorithms on the prepared database, new systems can be designed to help physicians that facilitate treatment processes.