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Data Mining in Clinical Data Sets: A Review

Shomona Gracia Jacob, R Geetha Ramani Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2012
© 2012 by IJAIS Journal
10.5120/ijais12-450774
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  1. Shomona Gracia Jacob and Geetha R Ramani. Article: Data Mining in Clinical Data Sets: A Review. International Journal of Applied Information Systems 4(6):15-26, December 2012. BibTeX

    @article{key:article,
    	author = "Shomona Gracia Jacob and R Geetha Ramani",
    	title = "Article: Data Mining in Clinical Data Sets: A Review",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 4,
    	number = 6,
    	pages = "15-26",
    	month = "December",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Data mining is one of the extensively researched areas in computer science and information technology owing to the wide influence exhibited by this computational technique on diverse fields that include finance, clinical research, multimedia, education and the like. Adequate survey and literature has been devoted to Clinical data mining, an active interdisciplinary area of research that is considered the consequent of applying artificial intelligence and data mining concepts to the field of medicine and health care. The aim of this research work is to provide a review on the foundation principles of mining clinical datasets, and present the findings and results of past researches on utilizing data mining techniques to mine health care data and patient records. The scope of this article is to present a brief report on preceding investigations made in the sphere of mining clinical data, the techniques applied and the conclusions recounted. Albeit extensive research has led to remarkable advancement in the field of clinical data mining and has paved the way for incredible enhancements in medical practice, the most recent research findings that can further unveil the potential of data mining in the realm of health care and medicine are clearly presented in this review.

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Keywords

Clinical data mining, Outlier Detection, Feature Selection, Clustering, Classification