ASSOCIATIVE CLASSIFICATION APPROACH CAN PREDICT PROSTATE CANCER BASED ON THE EXTRACTED ASSOCIATION RULES

Abstract

Aim: This study aims to classify the diagnosis status of prostate cancer and determine the related factors by applying the associative classification method, one of the data mining methods, to the open-access prostate cancer data set. Materials and Methods: In the current study , an open-access data set named "Prostate Cancer" is used for classification. The performance of the associative classification model is evaluated using the classification performance metrics such as sensitivity, selectivity, accuracy, balanced accuracy, negative predictive value, positive predictive value, and F1-score. Results: According to the prostate cancer classification results obtained from the associative classification model, the accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score values were obtained as 0.968, 0.789, 0.9, 0.879, 0.938, 0.882 and 0.923, respectively. Conclusion: In the analysis of the open-access data set, the proposed associative classification model has distinctively successful results in classifying prostate cancer on the performance metrics.

Keywords

• Prostate cancer
• data mining
• association rules
• classification
• associative classification

References

1. F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, "Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," CA: a cancer journal for clinicians, vol. 68, pp. 394-424, 2018.
2. A. Jemal, R. C. Tiwari, T. Murray, A. Ghafoor, A. Samuels, E. Ward, et al., "Cancer statistics, 2004," CA: a cancer journal for clinicians, vol. 54, pp. 8-29, 2004.
3. A. Jemal, A. Thomas, T. Murray, and M. Thun, "Cancer statistics, 2002," Ca-A Cancer Journal for Clinicians, vol. 52, pp. 23-47, 2002.
4. R. L. Siegel, K. D. Miller, and A. Jemal, "Cancer statistics, 2016," CA: a cancer journal for clinicians, vol. 66, pp. 7-30, 2016.
5. S. Özekes, "Veri madenciliği modelleri ve uygulama alanları," 2003.
6. A. K. Pujari, Data mining techniques: Universities press, 2001.
7. H. Akpınar, "Veri tabanlarında bilgi keşfi ve veri madenciliği," İstanbul Üniversitesi İşletme Fakültesi Dergisi, vol. 29, pp. 1-22, 2000.
8. L. Boney, A. Tewfik, and K. Hamdy, "Minimum association rule in large database," in Proceedings of Third IEEE International Conference on Computing, 2006, pp. 12-16.

9. S. Vinodh, N. H. Prakash, and K. E. Selvan, "Evaluation of leanness using fuzzy association rules mining," The International Journal of Advanced Manufacturing Technology, vol. 57, pp. 343-352, 2011.
10. 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.
11. Available: https://www.kaggle.com/sajidsaifi/prostate-cancer
12. Y. Köse, "Değerli müşterilerde ürün kategorileri arasındaki satış ilişkilerinin veri madenciliği yöntemlerinden birliktelik kuralları ve kümeleme analizi ile belirlenmesi ve ulusal bir perakendecide örnek uygulama," Selçuk Üniversitesi Sosyal Bilimler Enstitüsü, 2015.
13. A. S. Albayrak and S. K. Yilmaz, "VERİ MADENCİLİĞİ: KARAR AĞACI ALGORİTMALARI VE İMKB VERİLERİ ÜZERİNE BİR UYGULAMA," Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, vol. 14, 2009.
14. F. A. Thabtah, "A review of associative classification mining," Knowledge Engineering Review, vol. 22, pp. 37-65, 2007.
15. T. Kahvecioğlu and F. E. Güneş, "İnek sütü ve prostat kanseri ilişkisi," Sağlık ve Yaşam Bilimleri Dergisi, vol. 1, pp. 44-49, 2019.
16. A. Ahlbom, P. Lichtenstein, H. Malmström, M. Feychting, N. L. Pedersen, and K. Hemminki, "Cancer in twins: genetic and nongenetic familial risk factors," Journal of the National Cancer Institute, vol. 89, pp. 287-293, 1997.
17. U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, "Advances in knowledge discovery and data mining," 1996.
18. A. S. Kumar and R. Wahidabanu, "Data Mining Association Rules for Making Knowledgeable Decisions," in Data Mining Applications for Empowering Knowledge Societies, ed: IGI Global, 2009, pp. 43-53.
19. Available: https://www.kaggle.com/alihantabak/prostate-cancer-predictions-with-ml-and-dl-methods.