Identifying The Best-Fit Associative Classifier For Determining Survivability And Non-Survivability In Breast Cancer Patients
Keywords:
Classification, Breast cancer, Survivability, Associative ClassifierAbstract
In developing nations, cancer death is considered one of the biggest challenges. Even though there are various strategies to prevent cancer, some types of cancer still receive inadequate treatment. One of these is breast cancer. Early diagnosis is very important in treating this disease. Breast cancer has a good survival rate, especially when it is detected early. This can be attributed to better treatment and early diagnosis. This study aims to classify the various medical attributes collected from the SEER dataset for breast cancer patients into two categories: non-survivability and survivability. The paper used associative classifiers such as ACAC, ACN, L3, and CBA2 to analyze the data. The objective of the study is to identify a best fit classifier for deciding survivability and non-survivability in breast cancer patients based on accuracy. The outcome of the study revealed that CBA and CBA2 model exhibited an accuracy of 81% even in a huge dataset with 44325 records. The proposed approach also showed an improvement in performance. These findings indicated that the possibility of identifying non-survivability and survivability in breast cancer patients could be explored
Downloads
References
Van der Aalst W. “Process Mining: Data Science in Action”.Springer; Berlin/Heidelberg, Germany: 2016.
Romero C., Ventura S. “Educational data science in massive open online courses”. Wires Data Min. Knowl. Discov. 2017;7:1–12. doi: 10.1002/widm.1187.
Bray, F.; Ferlay, J.; Soerjomataram, I. “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries”. CA Cancer J. Clin. 2018, 68, 394–424.
Blake, C.L.; Merz, C.J. UCI Repository of Machine Learning Databases. 1998. Available online: http://www.ics.uci.edu/$sim$mlearn/MLRepository.html (accessed on 20 March 2020)
Chen, Hua, et al. "Classification prediction of breast cancer based on machine learning." Computational Intelligence and Neuroscience 2023 (2023).
Bazazeh, D.; Shubair, R. “Comparative study of machine learning algorithms for breast cancer detection and diagnosis”. In Proceedings of the 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), Ras Al Khaimah, UAE, 6–8 December 2016; pp. 1–4.
Chaurasia, Vikas, and Saurabh Pal. "Data mining techniques: to predict and resolve breast cancer survivability." International Journal of Computer Science and Mobile Computing IJCSMC 3.1 (2014): 10-22.
Delen, Dursun, Glenn Walker, and Amit Kadam. "Predicting breast cancer survivability: a comparison of three data mining methods." Artificial intelligence in medicine 34.2 (2005): 113-127.
Srinivas, K., G. Raghavendra Rao, and A. Govardhan. "Analysis of coronary heart disease and prediction of heart attack in coal mining regions using data mining techniques." 2010 5th International Conference on Computer Science & Education. IEEE, 2010.
Bernal, James Lopez, Steven Cummins, and Antonio Gasparrini. "Interrupted time series regression for the evaluation of public health interventions: a tutorial." International journal of epidemiology 46.1 (2017): 348-355.
Williams, Tom GS, et al. "Risk prediction models for colorectal cancer in people with symptoms: a systematic review." BMC gastroenterology 16 (2016): 1-16.
Oyewola, David, Danladi Hakimi, Kayode Adeboye, and Musa Danjuma Shehu. "Using five machine learning for breast cancer biopsy predictions based on mammographic diagnosis." International Journal of Engineering Technologies IJET 2, no. 4 (2016): 142-145.
Aruna, S., S. P. Rajagopalan, and L. V. Nandakishore. "Knowledge based analysis of various statistical tools in detecting breast cancer." Computer Science & Information Technology 2.2011 (2011): 37-45.
Zou Q, Qu K, Luo Y, Yin D, Ju Y, Tang H. “Predicting diabetes mellitus with machine learning techniques”. Frontiers in genetics. 2018 Nov 6;9:515.
Asri, Hiba, Hajar Mousannif, Hassan Al Moatassime, and Thomas Noel. "Using machine learning algorithms for breast cancer risk prediction and diagnosis." Procedia Computer Science 83 (2016): 1064-1069.
Wang, H.; Yoon, W.S. “Breast cancer prediction using data mining method”. In Proceedings of the 2015 Industrial and Systems Engineering Research Conference, Nashville, TN, USA, 30 May–2 June 2015.
Mangasarian, O.L.; Street, W.N.; Wolberg, W.H. “Breast cancer diagnosis and prognosis via linear programming”. Oper. Res. 1995, 43, 570–577.
Wolberg, W.H. Wisconsin Breast Cancer Database; University of Wisconsin Hospitals: Madison, WI, USA, 1991.
Nithya, R., and B. Santhi. "Classification of normal and abnormal patterns in digital mammograms for diagnosis of breast cancer." International journal of computer applications 28.6 (2011): 21-25.
Agarap, Abien Fred M. "On breast cancer detection: an application of machine learning algorithms on the wisconsin diagnostic dataset." Proceedings of the 2nd international conference on machine learning and soft computing. 2018.
Jirı, Filip, and Tomáš Kliegr. "Classification based on associations (cba)-a performance analysis." (2018).
Huang, Zaixiang, et al. "ACAC: Associative Classification based on All-Confidence." 2011 IEEE International Conference on Granular Computing. IEEE, 2011.
Li, Xueming, Dongxia Qin, and Cun Yu. "ACCF: Associative classification based on closed frequent itemsets." 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery. Vol. 2. IEEE, 2008.
Kundu, Gourab, et al. "ACN: An associative classifier with negative rules." 2008 11th IEEE International Conference on Computational Science and Engineering. IEEE, 2008.
Baralis, Elena, and Paolo Garza. "A lazy approach to pruning classification rules." 2002 IEEE International Conference on Data Mining, 2002. Proceedings.. IEEE, 2002.
Chen Guoqing, Liu Hongyan, Yu Lan, Wei Qiang, Zhang Xing. (2006) “A new approach to classification based on association rule mining.” Decision Support Systems 42(2): 674–689.
Deng Houtao, Runger George, Tuv Eugene, Bannister Wade. (2014) “CBC: an associative classifier with a small number of rules.” Decision Support Systems, 59(1): 163–170.
Ayyat Suzan, Lu Joan, Thabtah, Fadi Abdeljaber. (2014) “Class strength prediction method for associative classification.” in Proc. ARIA the Fourth International Conference on Advances in Information Mining and Management, pp. 5-10, France.
Alcala-Fdez, J., Alcala, R., Herrera, F.: “ A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning”. IEEE Transactions on Fuzzy Systems 19(5), 857–872 (2011)
M.J. Zaki, Ching-Jui Hsiao, “Charm: An efficient Algorithm for closed Association Rules Mining”, Technical Report, TR99-10.Computer Science Dept. Rensselaer Polytechnic Institute, 1999.
Fournier-Viger, Philippe, et al. "Spmf: a java open-source pattern mining library." J. Mach. Learn. Res. 15.1 (2014): 3389-3393.
Abubacker, Nirase Fathima, et al. "Breast cancer detection by using associative classifier with rule refinement method based on relevance feedback." Neural Computing and Applications 34.19 (2022): 16897-16910.
Behar, Nishant, and Manish Shrivastava. "A Novel Model for Breast Cancer Detection and Classification." Engineering, Technology & Applied Science Research 12.6 (2022): 9496-9502.
Wang, Kung-Jeng, Bunjira Makond, and Kung-Min Wang. "An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data." BMC medical informatics and decision making 13.1 (2013): 1-14.
Ponnuraja, Chinnaiyan, et al. "Decision tree classification and model evaluation for breast cancer survivability: A data mining approach." Biomedical and Pharmacology Journal 10.1 (2017): 281-289.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
