Volume 10, Issue 1 (Paramedical Sciences and Military Health (Summer 2015) 2015)                   Paramedical Sciences and Military Health 2015, 10(1): 39-48 | Back to browse issues page

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1- , mostafadamroodi1992@yahoo.com
Abstract:   (13624 Views)

Introduction: Extensive amounts of data stored in medical databases require the development of specialized tools for accessing the data, data analysis, knowledge discovery, and the effective use of the data. Data mining is one of the most important methods. The article sketches the used Data Mining techniques, and illustrates their applicability to medical diagnostic and prognostic problems.

Materials and Methods: The current study were searched English and Persian databases including Magiran, Iranmedex, SID, Google Scholar, OVID, Scopus, and PubMed by using keywords such as “Data Mining”, “Knowledge Discovery” and “Health”. Related articles were published and assessed during 1998-2013.

Findings: Data mining is a science that is searched automatically in the large amount of data for finding models and association rules in them where other statistical analysis cannot do that. The medical science is one of sciences that need to use of these tools for analyzing the large amount of data and creating predictive model with the new computation ways. In medical sciences, discovery and early diagnosis of the diseases can restrict the fatal diseases such as cancer and they save a people’s life. This research is shown that the data mining prediction provide necessary tools for the researcher and the physician to improve in the prevention of disease, diagnosis ways and their treatment programs.

Discussion and Conclusion: Nowadays, in the medical sciences, data collection of different diseases is very important. Recent development related to information technology & software has helped to have the better survey from producing massive data and could search the hidden knowledge in the data and create a new science by using different sciences including statistics, computers, and machine learning, and etc.

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Type of Study: review | Subject: article abstracts
Received: 2016/01/9 | Accepted: 2016/01/9 | Published: 2016/01/9

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