Discovering Constraint-based Sequential Patterns from Medical Datasets
M. Y. Alzahrani1, Fokrul A. Mazarbhuiya2
1Mohamed Y. AlZahrani, Department of Information Technology, Department of IT, AlBaha University, KSA.
2Fokrul Alom Mazarbhuiya*, Department of Mathematics, School of Fundamental and Applied Sciences, Assam Don Bosco University, Assam, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 724-728 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7011118419/2019©BEIESP | DOI: 10.35940/ijrte.D7011.118419
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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: The problem of mining sequential patterns from medical data has received a lot of attention as it aims to discover the causal relationship between different diseases or symptoms that are present in the patient’s body. Medical data contains the records pertaining to the information of the diseases or the symptoms of the patients besides the patients’ personal information. The records are ordered in accordance with the time and date of the patients’ visit in the hospital. Such data may offer precious information related to the cause and effect of a disease on the human body. Although, the date and time gives us chronological ordering of the occurrence of the diseases in the human body, it does not provide the information about the time intervals within which the successive diseases may occur. If the time gap of cause and effect is found to be too large, the concerned sequential pattern would be un-realistic. Considering, the time attributes of medical data, we try to address the above-mentioned problem on the sequential patterns. In this paper, we propose a method of extracting sequential patterns from medical dataset, with time-restrictions. The method extracts all sequences of diseases which occur within user-specified time intervals. The efficacy of our method is established with an experiment conducted on real life medical datasets.
Keywords: Data Mining, Sequential Patterns Mining, Constraint Sequential Pattern Mining, Frequent Sequence, Maximal Sequence, Frequent Sequence within time intervals, Disease, Set of Symptoms, Frequent diseases.
Scope of the Article: Data Mining.