An Optimized Model for Breast Cancer Prediction Using Frequent Itemsets Mining

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Author(s)

Ankita Sinha 1,* Bhaswati Sahoo 1 Siddharth Swarup Rautaray 1 Manjusha Pandey 1

1. School of Computer Engineering, KIIT Deemed University, Bhubaneswar, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2019.05.02

Received: 2 May 2019 / Revised: 18 May 2019 / Accepted: 30 May 2019 / Published: 8 Sep. 2019

Index Terms

Association rule mining, Frequent itemsets mining, Decision tree, Naive bayes, Support vector machine, k-nearest neighbour, Prediction

Abstract

This presented research paper mainly studies the frequent itemsets mining approach for finding the most important attribute to overcome the existing problems in the extraction of relevant information by using data mining approaches from a huge amount of dataset. Firstly a state of art diagram for prediction is designed and data mining classifier like naive bayes, support vector machine, decision tree, k- nearest neighbour are compared and then proposed methodology with new techniques are proposed. Moreover, a new attribute filtering association frequent itemsets mining algorithm is presented. Then, by analyzing the feasibility of the proposed algorithm, the data mining classification classifier is compared. As a result, SVM produces the best result among all the classifier with attribute filtrating and without attribute filtrating. With attribute filtrating algorithm enhances the accuracy of all the other classifier.

Cite This Paper

Ankita Sinha, Bhaswati Sahoo, Siddharth Swarup Rautaray, Manjusha Pandey, "An Optimized Model for Breast Cancer Prediction Using Frequent Itemsets Mining", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.11, No.5, pp. 11-18, 2019. DOI:10.5815/ijieeb.2019.05.02

Reference

[1]Umadevi, S. and Marseline, K.J., 2017, July. A survey on data mining classification algorithms. In 2017 International Conference on Signal Processing and Communication (ICSPC) (pp. 264-268). IEEE.
[2]Wani, N.U.H., Taneja, K. and Adlakha, N., 2013. Health System in India: Opportunities and Challenges for Enhancements. IOSR Journal of Business and Management (IOSR-JBM), 9(2), pp.74-82.
[3]https://www.medicinenet.com/breast_cancer_facts_stages/article.htm#are_there_any_other_questions_i_should_ask_my_doctor
_about_breast_cancer--> Breast Cancer.
[4]Breast Cancer -- https://www.medicalnewstoday.com/articles/37136.php.
[5]Sakri, S.B., Rashid, N.B.A. and Zain, Z.M., 2018. Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access, 6, pp.29
[6]Alwidian, J., Hammo, B.H. and Obeid, N., 2018. WCBA: Weighted classification based on association rules algorithm for breast cancer disease. Applied Soft Computing, 62, pp.536-549.
[7]Shukla, N., Hagenbuchner, M., Win, K.T. and Yang, J., 2018. Breast cancer data analysis for survivability studies and prediction. Computer methods and programs in biomedicine, 155, pp.199-208.
[8]Park, K., Ali, A., Kim, D., An, Y., Kim, M. and Shin, H., 2013. Robust predictive model for evaluating breast cancer survivability. Engineering Applications of Artificial Intelligence, 26(9), pp.2194-2205.
[9]Asri, H., Mousannif, H., Al Moatassime, H. and Noel, T., 2016. Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Computer Science, 83, pp.1064-1069.
[10]Shah, C. and Jivani, A.G., 2013, July. Comparison of data mining classification algorithms for breast cancer prediction. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-4). IEEE.
[11]Tripathy, P., Rautaray, S.S. and Pandey, M., 2017, February. Parallel support vector machine used in map-reduce for risk analysis. In 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-4). IEEE.
[12]Bhardwaj, A. and Tiwari, A., 2015. Breast cancer diagnosis using genetically optimized neural network model. Expert Systems with Applications, 42(10), pp.4611-4620.
[13]Gupta, S., Kumar, D. and Sharma, A., 2011. Data mining classification techniques applied for breast cancer diagnosis and prognosis. Indian Journal of Computer Science and Engineering (IJCSE), 2(2), pp.188-195.
[14]Agarwal, S., 2013, December. Data mining: data mining concepts and techniques. In 2013 International Conference on Machine Intelligence and Research Advancement (pp. 203-207). IEEE.
[15]Han, J., Pei, J. and Kamber, M., 2011. Data mining: concepts and techniques. Elsevier.
[16]Umadevi, S. and Marseline, K.J., 2017, July. A survey on data mining classification algorithms. In 2017 International Conference on Signal Processing and Communication (ICSPC) (pp. 264-268). IEEE.
[17]Database;http://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic).