Breast Cancer Diagnosis using Sequential Pattern Mining
Fokrul A. Mazarbhuiya1, M. Y. Alzahrani2, Ahmad H. Alahmadi3
1Fokrul Alom Mazarbhuiya, Department of Mathematics, School of Fundamental and Applied Sciences, Assam Don Bosco University, Assam, India.
2Mohamed Y. AlZahrani*, Department of Information Technology, AlBaha University, KSA.
3Ahmad H. Alahmadi, Department of Computer Science and Information,Taibah University, Medina, KSA,
Manuscript received on February 28, 2020. | Revised Manuscript received on March 22, 2020. | Manuscript published on March 30, 2020. | PP: 5515-5519 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9171038620/2020©BEIESP | DOI: 10.35940/ijrte.F9171.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cancer is a disease very common to both rural and urban peoples. It is the abnormal growth of some body cells which then destroy the normal functioning of surrounding cells. Cancer has different stages and can be cured easily if diagnosed earlier. Breast Cancer is widespread among the women of different age groups which results untimely death of so many ladies. As the cause of every cancer occurs before its actual appearance in the human body, sequential pattern from cancer datasets can be useful for determining the cause of Breast Cancer before its actual occurrence in the women body. In this article, we put forward a technique for digging out such patterns from Breast Cancer data. The effectiveness of the proposed technique is demonstrated by the experimental studies made with a real Breast Cancer dataset.
Keywords: Mining Sequence, Frequent Sequence, Maximal Frequent Sequence, Disease, Symptoms of a disease, Signs of a disease, Breast Cancer.
Scope of the Article: Text Mining.