Comparison of Different Machine Learning Algorithms for Breast Cancer Recurrence Classification
Abstract
In this paper we compared some machine learning algorithms to predict recurrence of breast cancer and see which model used gives best accuracy for the prediction. In this study we used database donated by University Medical Centre, Institute of Oncology, Ljubljana, Slovenia. The preprocessed dataset includes 286 instances, 9 attributes and 1 class attribute. Firstly, we used attribute evaluation to see which attribute is more effective on class attribute. Secondly we have explored three different algorithms: C4.5, Random Forest and K Nearest Neighbor. Several data mining tools have been applied with these 3 algorithms to explore which model is better on accuracy. Finally we have found that C4.5 algorithm is the best for our dataset: breast cancer recurrence.
Keywords
data mining; breast cancer; classification; machine learning; Weka; C4.5; random forest; KNN
Full Text:
PDFDOI: http://dx.doi.org/10.21533/scjournal.v8i2.179
Refbacks
- There are currently no refbacks.
Copyright (c) 2019 M Haskul
ISSN 2233 -1859
Digital Object Identifier DOI: 10.21533/scjournal
This work is licensed under a Creative Commons Attribution 4.0 International License