Design ensemble machine learning model for breast cancer diagnosis
Journal
Journal of Medical Systems
Journal Volume
36
Journal Issue
5
Pages
2841-2847
Date Issued
2012
Author(s)
Abstract
In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models. ? 2011 Springer Science+Business Media, LLC.
Subjects
Ensemble learning; Information gain; KNN; Neural fuzzy; Quadratic classifier
SDGs
Other Subjects
article; breast cancer; cancer classification; cancer diagnosis; classifier; diagnostic accuracy; ensemble machine learning; fuzzy system; human; k nearest neighbor; machine learning; validation process; artificial neural network; breast tumor; cell adhesion; cell shape; cell size; computer assisted diagnosis; female; fuzzy logic; methodology; radiography; Breast Neoplasms; Cell Adhesion; Cell Shape; Cell Size; Diagnosis, Computer-Assisted; Female; Fuzzy Logic; Humans; Neural Networks (Computer)
Type
journal article