Prediction of benign and malignant breast cancer using data mining techniques

Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common i...

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Published in:	Journal of algorithms & computational technology Vol. 12; no. 2; pp. 119 - 126
Main Authors:	Chaurasia, Vikas, Pal, Saurabh, Tiwari, BB
Format:	Journal Article
Language:	English
Published:	London, England SAGE Publications 01.06.2018 Sage Publications Ltd SAGE Publishing
Subjects:	Accuracy Algorithms Breast cancer Data mining Datasets Income Measurement methods Scientific papers Survivability Urbanization Naïve Bayes RBF Network Breast cancer data mining J48
ISSN:	1748-3026, 1748-3018, 1748-3026
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Abstract	Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.
AbstractList	Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.
Author	Pal, Saurabh Chaurasia, Vikas Tiwari, BB
Author_xml	– sequence: 1 givenname: Vikas surname: Chaurasia fullname: Chaurasia, Vikas email: chaurasia.vikas@gmail.com organization: Department of MCA, VBS Purvanchal University, Jaunpur, India – sequence: 2 givenname: Saurabh surname: Pal fullname: Pal, Saurabh organization: Department of MCA, VBS Purvanchal University, Jaunpur, India – sequence: 3 givenname: BB surname: Tiwari fullname: Tiwari, BB organization: Department. of ECE, Faculty of Engg. & Technology, VBS Purvanchal University, Jaunpur
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SubjectTerms	Accuracy Algorithms Breast cancer Data mining Datasets Income Measurement methods Scientific papers Survivability Urbanization
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Title	Prediction of benign and malignant breast cancer using data mining techniques
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