DECISION TREE CLASSIFIERS FOR CLASSIFICATION OF BREAST CANCER


P. Hamsagayathri, P. Sampath

Abstract


Objective: Breast cancer is one of the dangerous cancers among world’s women above 35 y. The breast is made up of lobules that secrete milk and thin milk ducts to carry milk from lobules to the nipple. Breast cancer mostly occurs either in lobules or in milk ducts. The most common type of breast cancer is ductal carcinoma where it starts from ducts and spreads across the lobules and surrounding tissues. Survey: According to the medical survey, each year there are about 125.0 per 100,000 new cases of breast cancer are diagnosed and 21.5 per 100,000 women due to this disease in united states. Also, 246,660 new cases of women with cancer are estimated for the year 2016.

Methods: Early diagnosis of breast cancer is a key factor for long-term survival of cancer patients. Classification is one of the vital techniques used by researchers to analyze and classify the medical data.

Results: This paper analyzes the different decision tree classifier algorithms for seer breast cancer dataset using WEKA software. The performance of the classifiers are evaluated against the parameters like accuracy, Kappa statistic, Entropy, RMSE, TP Rate, FP Rate, Precision, Recall, F-Measure, ROC, Specificity, Sensitivity.

Conclusion: The simulation results shows REPTree classifier classifies the data with 93.63% accuracy and minimum RMSE of 0.1628 REPTree algorithm consumes less time to build the model with 0.929 ROC and 0.959 PRC values. By comparing classification results, we confirm that a REPTree algorithm is better than other classification algorithms for SEER dataset.


Keywords


Classification, J48, REP Tree, Random Forest, Random Tree, Accuracy, RMSE, Confusion matrix

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References


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About this article

Title

DECISION TREE CLASSIFIERS FOR CLASSIFICATION OF BREAST CANCER

Keywords

Classification, J48, REP Tree, Random Forest, Random Tree, Accuracy, RMSE, Confusion matrix

DOI

10.22159/ijcpr.2017v9i1.17377

Date

01-03-2017

Additional Links

Manuscript Submission

Journal

International Journal of Current Pharmaceutical Research
Vol 9, Issue 2, 2017 Page: 31-36

Online ISSN

0975-7066

Statistics

103 Views | 73 Downloads

Authors & Affiliations

P. Hamsagayathri
Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, Erode

P. Sampath


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