Association rule mining based study for identification of clinical parameters akin to occurrence of brain tumor

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Title	Association rule mining based study for identification of clinical parameters akin to occurrence of brain tumor
Authors	Dipankar Sengupta¹*, Meemansa Sood¹, Poorvika Vijayvargia¹, Sunil Hota² & Pradeep K Naik¹
Affiliation	¹Dept. of Biotechnology & Bioinformatics, Jaypee University of Information Technology, Waknaghat, Solan, H.P., India; ²DIHAR, Defense Research & Development Organization, Leh, Jammu & Kashmir, India.
Email	dipankarsengupta.1982@gmail.com; *Corresponding author
Article Type	Hypothesis
Date	Received June 13, 2013; Accepted June 16, 2013; Published June 29, 2013
Abstract	Healthcare sector is generating a large amount of information corresponding to diagnosis, disease identification and treatment of an individual. Mining knowledge and providing scientific decision-making for the diagnosis & treatment of disease from the clinical dataset is therefore increasingly becoming necessary. Aim of this study was to assess the applicability of knowledge discovery in brain tumor data warehouse, applying data mining techniques for investigation of clinical parameters that can be associated with occurrence of brain tumor. In this study, a brain tumor warehouse was developed comprising of clinical data for 550 patients. Apriori association rule algorithm was applied to discover associative rules among the clinical parameters. The rules discovered in the study suggests - high values of Creatinine, Blood Urea Nitrogen (BUN), SGOT & SGPT to be directly associated with tumor occurrence for patients in the primary stage with atleast 85% confidence and more than 50% support. A normalized regression model is proposed based on these parameters along with Haemoglobin content, Alkaline Phosphatase and Serum Bilirubin for prediction of occurrence of STATE (brain tumor) as 0 (absent) or 1 (present). The results indicate that the methodology followed will be of good value for the diagnostic procedure of brain tumor, especially when large data volumes are involved and screening based on discovered parameters would allow clinicians to detect tumors at an early stage of development.
Keywords	Apriori algorithm; Association Mining; Brain Tumor Data warehouse; Data Mining.
Citation	*Sengupta et al.* Bioinformation 9(11): 555-559 (2013)
Edited by	P Kangueane
ISSN	0973-2063
Publisher	Biomedical Informatics
License	This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.