Research Article
Year 2020,
Volume: 5 Issue: 1, 33 - 40, 30.06.2020
Abstract
References
- K. Oktay et al., "A Computational Statistics Approach to Evaluate Blood Biomarkers for Breast Cancer Risk Stratification," Hormones and Cancer, vol. 11, no. 1, pp. 17-33, 2020.
- J. Ping et al., "Differences in gene-expression profiles in breast cancer between African and European-ancestry women," Carcinogenesis, 2020.
- H. Akpınar, "Veri tabanlarında bilgi keşfi ve veri madenciliği," İÜ İşletme Fakültesi Dergisi, vol. 29, no. 1, pp. 1-22, 2000.
- A. Koyuncugil and N. Özgülbaş, "Veri madenciliği: Tıp ve sağlık hizmetlerinde kullanımı ve uygulamaları," Bilişim Teknolojileri Dergisi, vol. 2, no. 2, 2009.
- L. T. Moss and S. Atre, Business intelligence roadmap: the complete project lifecycle for decision-support applications. Addison-Wesley Professional, 2003.
- Y.-L. Chen, J.-M. Chen, and C.-W. Tung, "A data mining approach for retail knowledge discovery with consideration of the effect of shelf-space adjacency on sales," Decision support systems, vol. 42, no. 3, pp. 1503-1520, 2006.
- A. K. Pujari, Data mining techniques. Universities press, 2001.
- F. Thabtah, "A review of associative classification mining," The Knowledge Engineering Review, vol. 22, no. 1, pp. 37-65, 2007.
- D. Dua and C. Graff, "UCI machine learning repository. School of Information and Computer Science, University of California, Irvine, CA," ed, 2019.
- A. S. Kumar and R. Wahidabanu, "Data Mining Association Rules for Making Knowledgeable Decisions," in Data Mining Applications for Empowering Knowledge Societies: IGI Global, 2009, pp. 43-53.
- U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, "Advances in knowledge discovery and data mining," 1996: American Association for Artificial Intelligence.
- D. T. Larose and C. D. Larose, Discovering knowledge in data: an introduction to data mining. John Wiley & Sons, 2014.
- R. Agrawal, T. Imieliński, and A. Swami, "Mining association rules between sets of items in large databases," in Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 1993, pp. 207-216.
- J. Han and M. Kamber, "Data Mining: Concepts and Tecniques. ISBN 13: 978-1-55860-901-3," ed: Elsevier, USA, 2008.
- M. Houtsma and A. Swami, "Set-oriented mining for association rules in relational databases," in Proceedings of the eleventh international conference on data engineering, 1995, pp. 25-33: IEEE.
- R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proc. 20th int. conf. very large data bases, VLDB, 1994, vol. 1215, pp. 487-499.
- A. Savasere, E. R. Omiecinski, and S. B. Navathe, "An efficient algorithm for mining association rules in large databases," Georgia Institute of Technology1995.
- A. Das, W.-K. Ng, and Y.-K. Woon, "Rapid association rule mining," in Proceedings of the tenth international conference on Information and knowledge management, 2001, pp. 474-481.
- M. J. Zaki and C.-J. Hsiao, "CHARM: An efficient algorithm for closed itemset mining," in Proceedings of the 2002 SIAM international conference on data mining, 2002, pp. 457-473: SIAM.
- N. Ye, The handbook of data mining. CRC Press, 2003.
- M. Azmi, G. C. Runger, and A. Berrado, "Interpretable regularized class association rules algorithm for classification in a categorical data space," Information Sciences, vol. 483, pp. 313-331, 2019.
- W. Chang, J. Cheng, J. Allaire, Y. Xie, and J. McPherson, "Shiny: web application framework for R," R package version, vol. 1, no. 5, 2017
- W. Chang, T. Park, L. Dziedzic, N. Willis, and M. McInerney, "shinythemes: Themes for Shiny," R package version, vol. 1, no. 1, p. 144, 2015.
- E. Bailey, "shinyBS: Twitter bootstrap components for shiny," R package version 0.61, 2015.
- J. Dumas, "shinyLP: Bootstrap Landing Home Pages for Shiny Applications," R package version, vol. 1, p. 2, 2019.
- D. Attali and T. Edwards, "shinyalert: Easily Create Pretty Popup Messages (Modals) in'Shiny'," R package version 1.0, 2018.
- D. Attali, "Shinyjs: Easily improve the user experience of your shiny apps in seconds," R package version 0.9, 2016.
- M. B. Kursa and W. R. Rudnicki, "Feature selection with the Boruta package," J Stat Softw, vol. 36, no. 11, pp. 1-13, 2010.
- M. Hahsler et al., "Package ‘arules’," ed, 2019.
- I. Johnson, "arulesCBA: Classification for Factor and Transactional Data Sets Using Association Rules."
- M. Kuhn, "The caret package," R Foundation for Statistical Computing, Vienna, Austria. URL https://cran. r-project. org/package= caret, 2012.
- B. Almende, B. Thieurmel, and T. Robert, "visNetwork: Network Visualization using “vis. js” Library," ed: CRAN, 2016.
- M. Toğaçar, B. Ergen, and Z. Cömert, "Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders," Medical Hypotheses, vol. 135, p. 109503, 2020/02/01/ 2020.
- İ. Perçın, F. H. Yağin, E. Güldoğan, and S. Yoloğlu, "ARM: An Interactive Web Software for Association Rules Mining and an Application in Medicine," in 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), 2019, pp. 1-5: IEEE.
- İ. PERÇİN, F. H. YAĞIN, A. K. ARSLAN, and C. ÇOLAK, "An Interactive Web Tool for Classification Problems Based on Machine Learning Algorithms Using Java Programming Language: Data Classification Software," in 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2019, pp. 1-7: IEEE.
- G. Rawal, R. Rawal, H. Shah, and K. Patel, "A Comparative Study Between Artificial Neural Networks and Conventional Classifiers for Predicting Diagnosis of Breast Cancer," in ICDSMLA 2019: Springer, 2020, pp. 261-271.
- M. Abdar et al., "A new nested ensemble technique for automated diagnosis of breast cancer," vol. 132, pp. 123-131, 2020.
- N. Vutakuri and A. U. J. I. J. o. A. I. P. Maheswari, "Breast cancer diagnosis using a Minkowski distance method based on mutual information and genetic algorithm," vol. 16, no. 3-4, pp. 414-433, 2020.
- P. S. Nishant, S. Mehrotra, B. G. K. Mohan, and G. Devaraju, "Identifying Classification Technique for Medical Diagnosis," in ICT Analysis and Applications: Springer, 2020, pp. 95-104.
- N. Panwar, D. Sharma, and N. J. A. a. S. Narang, "Breast Cancer Classification with Machine Learning Classifier Techniques," 2020.
A NOVEL INTERPRETABLE WEB-BASED TOOL ON THE ASSOCIATIVE CLASSIFICATION METHODS: AN APPLICATION ON BREAST CANCER DATASET
Abstract
Aim: The second-largest cause of cancer mortality for women is breast cancer. The main techniques for diagnosing breast cancer are mammography and tumor biopsy accompanied by histopathological studies. The mammograms are not detective of all subtypes of breast tumors, particularly those which arise and are more aggressive in young women or women with dense breast tissue. Circulating prognostic molecules and liquid biopsy approaches to detect breast cancer and the death risk are desperately essential. The purpose of this study is to develop a web-based tool for the use of the associative classification method that can classify breast cancer using the association rules method.
Materials and Methods: In this study, an open-access dataset named “Breast Cancer Wisconsin (Diagnostic) Data Set” was used for the classification. To create this web-based application, the Shiny library is used, which allows the design of interactive web-based applications based on the R programming language. Classification based on association rules (CBAR) and regularized class association rules (RCAR) are utilized to classify breast cancer (malignant/benign) based on the generated rules.
Results: Based on the classification results of breast cancer, accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score values obtained from the CBAR model are 0.954, 0.951, 0.939, 0.964, 0.939, 0.964, and 0.939 respectively.
Conclusion: In the analysis of the open-access dataset, the proposed model has a distinctive feature in classifying breast cancer based on the performance metrics. The associative classification software developed based on CBAR produces successful predictions in the classification of breast cancer. The hypothesis established within the scope of the purpose of this study has been confirmed as the similar estimates are achieved with the results of other papers in the classification of breast cancer.
Keywords
Artificial intelligence association rules associative classification web-based software breast cancer
References
- K. Oktay et al., "A Computational Statistics Approach to Evaluate Blood Biomarkers for Breast Cancer Risk Stratification," Hormones and Cancer, vol. 11, no. 1, pp. 17-33, 2020.
- J. Ping et al., "Differences in gene-expression profiles in breast cancer between African and European-ancestry women," Carcinogenesis, 2020.
- H. Akpınar, "Veri tabanlarında bilgi keşfi ve veri madenciliği," İÜ İşletme Fakültesi Dergisi, vol. 29, no. 1, pp. 1-22, 2000.
- A. Koyuncugil and N. Özgülbaş, "Veri madenciliği: Tıp ve sağlık hizmetlerinde kullanımı ve uygulamaları," Bilişim Teknolojileri Dergisi, vol. 2, no. 2, 2009.
- L. T. Moss and S. Atre, Business intelligence roadmap: the complete project lifecycle for decision-support applications. Addison-Wesley Professional, 2003.
- Y.-L. Chen, J.-M. Chen, and C.-W. Tung, "A data mining approach for retail knowledge discovery with consideration of the effect of shelf-space adjacency on sales," Decision support systems, vol. 42, no. 3, pp. 1503-1520, 2006.
- A. K. Pujari, Data mining techniques. Universities press, 2001.
- F. Thabtah, "A review of associative classification mining," The Knowledge Engineering Review, vol. 22, no. 1, pp. 37-65, 2007.
- D. Dua and C. Graff, "UCI machine learning repository. School of Information and Computer Science, University of California, Irvine, CA," ed, 2019.
- A. S. Kumar and R. Wahidabanu, "Data Mining Association Rules for Making Knowledgeable Decisions," in Data Mining Applications for Empowering Knowledge Societies: IGI Global, 2009, pp. 43-53.
- U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, "Advances in knowledge discovery and data mining," 1996: American Association for Artificial Intelligence.
- D. T. Larose and C. D. Larose, Discovering knowledge in data: an introduction to data mining. John Wiley & Sons, 2014.
- R. Agrawal, T. Imieliński, and A. Swami, "Mining association rules between sets of items in large databases," in Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 1993, pp. 207-216.
- J. Han and M. Kamber, "Data Mining: Concepts and Tecniques. ISBN 13: 978-1-55860-901-3," ed: Elsevier, USA, 2008.
- M. Houtsma and A. Swami, "Set-oriented mining for association rules in relational databases," in Proceedings of the eleventh international conference on data engineering, 1995, pp. 25-33: IEEE.
- R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proc. 20th int. conf. very large data bases, VLDB, 1994, vol. 1215, pp. 487-499.
- A. Savasere, E. R. Omiecinski, and S. B. Navathe, "An efficient algorithm for mining association rules in large databases," Georgia Institute of Technology1995.
- A. Das, W.-K. Ng, and Y.-K. Woon, "Rapid association rule mining," in Proceedings of the tenth international conference on Information and knowledge management, 2001, pp. 474-481.
- M. J. Zaki and C.-J. Hsiao, "CHARM: An efficient algorithm for closed itemset mining," in Proceedings of the 2002 SIAM international conference on data mining, 2002, pp. 457-473: SIAM.
- N. Ye, The handbook of data mining. CRC Press, 2003.
- M. Azmi, G. C. Runger, and A. Berrado, "Interpretable regularized class association rules algorithm for classification in a categorical data space," Information Sciences, vol. 483, pp. 313-331, 2019.
- W. Chang, J. Cheng, J. Allaire, Y. Xie, and J. McPherson, "Shiny: web application framework for R," R package version, vol. 1, no. 5, 2017
- W. Chang, T. Park, L. Dziedzic, N. Willis, and M. McInerney, "shinythemes: Themes for Shiny," R package version, vol. 1, no. 1, p. 144, 2015.
- E. Bailey, "shinyBS: Twitter bootstrap components for shiny," R package version 0.61, 2015.
- J. Dumas, "shinyLP: Bootstrap Landing Home Pages for Shiny Applications," R package version, vol. 1, p. 2, 2019.
- D. Attali and T. Edwards, "shinyalert: Easily Create Pretty Popup Messages (Modals) in'Shiny'," R package version 1.0, 2018.
- D. Attali, "Shinyjs: Easily improve the user experience of your shiny apps in seconds," R package version 0.9, 2016.
- M. B. Kursa and W. R. Rudnicki, "Feature selection with the Boruta package," J Stat Softw, vol. 36, no. 11, pp. 1-13, 2010.
- M. Hahsler et al., "Package ‘arules’," ed, 2019.
- I. Johnson, "arulesCBA: Classification for Factor and Transactional Data Sets Using Association Rules."
- M. Kuhn, "The caret package," R Foundation for Statistical Computing, Vienna, Austria. URL https://cran. r-project. org/package= caret, 2012.
- B. Almende, B. Thieurmel, and T. Robert, "visNetwork: Network Visualization using “vis. js” Library," ed: CRAN, 2016.
- M. Toğaçar, B. Ergen, and Z. Cömert, "Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders," Medical Hypotheses, vol. 135, p. 109503, 2020/02/01/ 2020.
- İ. Perçın, F. H. Yağin, E. Güldoğan, and S. Yoloğlu, "ARM: An Interactive Web Software for Association Rules Mining and an Application in Medicine," in 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), 2019, pp. 1-5: IEEE.
- İ. PERÇİN, F. H. YAĞIN, A. K. ARSLAN, and C. ÇOLAK, "An Interactive Web Tool for Classification Problems Based on Machine Learning Algorithms Using Java Programming Language: Data Classification Software," in 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2019, pp. 1-7: IEEE.
- G. Rawal, R. Rawal, H. Shah, and K. Patel, "A Comparative Study Between Artificial Neural Networks and Conventional Classifiers for Predicting Diagnosis of Breast Cancer," in ICDSMLA 2019: Springer, 2020, pp. 261-271.
- M. Abdar et al., "A new nested ensemble technique for automated diagnosis of breast cancer," vol. 132, pp. 123-131, 2020.
- N. Vutakuri and A. U. J. I. J. o. A. I. P. Maheswari, "Breast cancer diagnosis using a Minkowski distance method based on mutual information and genetic algorithm," vol. 16, no. 3-4, pp. 414-433, 2020.
- P. S. Nishant, S. Mehrotra, B. G. K. Mohan, and G. Devaraju, "Identifying Classification Technique for Medical Diagnosis," in ICT Analysis and Applications: Springer, 2020, pp. 95-104.
- N. Panwar, D. Sharma, and N. J. A. a. S. Narang, "Breast Cancer Classification with Machine Learning Classifier Techniques," 2020.
There are 40 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Electrical Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | June 30, 2020 |
| Published in Issue | Year 2020 Volume: 5 Issue: 1 |
