TITLE:
Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic
AUTHORS:
Cuong Nguyen, Yong Wang, Ha Nam Nguyen
KEYWORDS:
Breast Cancer; Diagnosis; Prognosis; Feature Selection; Random Forest
JOURNAL NAME:
Journal of Biomedical Science and Engineering,
Vol.6 No.5,
May
25,
2013
ABSTRACT: As
the incidence of this disease has increased significantly in the recent
years, expert systems and machine learning techniques to this problem have
also taken a great attention from many scholars. This study aims at diagnosing
and prognosticating breast cancer with a machine learning method based on random
forest classifier and feature selection technique. By weighting, keeping useful
features and removing redundant features in datasets, the method was obtained
to solve diagnosis problems via classifying Wisconsin Breast Cancer Diagnosis
Dataset and to solve prognosis problem via classifying Wisconsin Breast Cancer
Prognostic Dataset. On these datasets we obtained classification accuracy of
100% in the best case and of around 99.8% on average. This is very promising
compared to the previously reported results. This result is for Wisconsin
Breast Cancer Dataset but it states that this method can be used confidently
for other breast cancer diagnosis problems, too.