KNOWLEDGE DISCOVERY IN DATABASES (KDD)

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PIRI Z.; ASEFZADEH S.

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Journal: JOURNAL OF TRANSLATIONAL MEDICAL RESEARCH Year:2003 | Volume:10 | Issue:2 Page(s): 10-16

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Title

KNOWLEDGE DISCOVERY IN DATABASES (KDD)

Author(s)

PIRI Z. | ASEFZADEH S. | Issue Writer Certificate

Keywords

KDD; DATABASES; KNOWLEDGE DISCOVERY; HEALTHCARE ORGANIZATIONSQ4

Abstract

Background and Aim: The main challenge in each organization, in particular healthcare organizations deal is effective utilization of information stored in their various information systems. Large amounts of data will have organizational value as they convert to a knowledge which help the organizations in cost controlling, profit increasing as well as maintaining the quality of patient care. The aim of this study was to determine initiatives and methods for effective utilization of data and information stored in databases of healthcare organizations in developed countries.
Materials and Methods: Through systematic review, the articles regarding KDD published since 1990 were reviewed. After studying books and journals related to data and databases in the libraries of Iran University of medical sciences, central library of Amir Kabir University, library of Science and Industry University, articles published by databases such as ScienceDirect, Pubmed, Proquest, Blakwell as well as the sites of international organizations working on knowledge discovery and data mining were also reviewed.
Results: Necessity for effective utilization of large amount of data has been the subject of a field called Knowledge Discovery in Databases (KDD). Extensive initiatives have been done in developed countries and distinctive steps have been determined for knowledge discovery since 1989. Fraud detection, marketing and customer retention are some examples of KDD applications in several industries. Similar applications regarding patient-oriented and quality improvement in healthcare are examples of KDD applications in the healthcare industry. Using capabilities of various sciences, several approaches have been developed by which current soft wares are extracting hidden knowledge from large databases. Quantitative approaches such as Probabilistic and statistical approaches, classification approaches such as Bayesian, decision tree and other methods including deviation and Trend Analysis, generic algorithms, neural networks and hybrid methods which use two or more techniques are used in KDD tools.
Conclusion: Healthcare organizations can discover hidden knowledge in databases by using software programs developed based on one or more KDD techniques, and along with cost reduction, they can use its interesting results for decision making in their activities.

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APA: Copy

PIRI, Z., & ASEFZADEH, S.. (2003). KNOWLEDGE DISCOVERY IN DATABASES (KDD). JOURNAL OF BIRJAND UNIVERSITY OF MEDICAL SCIENCES, 10(2), 10-16. SID. https://sid.ir/paper/95483/en

Vancouver: Copy

PIRI Z., ASEFZADEH S.. KNOWLEDGE DISCOVERY IN DATABASES (KDD). JOURNAL OF BIRJAND UNIVERSITY OF MEDICAL SCIENCES[Internet]. 2003;10(2):10-16. Available from: https://sid.ir/paper/95483/en

IEEE: Copy

Z. PIRI, and S. ASEFZADEH, “KNOWLEDGE DISCOVERY IN DATABASES (KDD),” JOURNAL OF BIRJAND UNIVERSITY OF MEDICAL SCIENCES, vol. 10, no. 2, pp. 10–16, 2003, [Online]. Available: https://sid.ir/paper/95483/en

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