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Data Mining Algorithms and Medical Sciences


 

Extensive amounts of data stored in medical databases require the development of dedicated tools for accessing the data, data analysis, knowledge discovery, and effective use of sloretl knowledge and data. Widespread use of medical information systems and explosive enlargement of medical databases require conventional manual data analysis to be coupled with methods for competent computer-assisted analysis. In this paper, I use Data Mining techniques for the data analysis, data accessing and knowledge discovery procedure to show experimentally and practically that how consistent, able and fast are these techniques for the study in the particular field? A solid mathematical threshold (0 to 1) is set to analyze the data. The obtained outcome will be tested by applying the approach to the databases, data warehouses and any data storage of different sizes with different entry values. The results shaped will be of different level from short to the largest sets of tuple. By this, we may take the results formed for different use e.g. Patient investigation, frequency of different disease.

Keywords

Knowledge Discovery, Medical Database, Association Rule Mining Techniques, Analysis, Transformation.
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  • Data Mining Algorithms and Medical Sciences

Abstract Views: 339  |  PDF Views: 148

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Abstract


Extensive amounts of data stored in medical databases require the development of dedicated tools for accessing the data, data analysis, knowledge discovery, and effective use of sloretl knowledge and data. Widespread use of medical information systems and explosive enlargement of medical databases require conventional manual data analysis to be coupled with methods for competent computer-assisted analysis. In this paper, I use Data Mining techniques for the data analysis, data accessing and knowledge discovery procedure to show experimentally and practically that how consistent, able and fast are these techniques for the study in the particular field? A solid mathematical threshold (0 to 1) is set to analyze the data. The obtained outcome will be tested by applying the approach to the databases, data warehouses and any data storage of different sizes with different entry values. The results shaped will be of different level from short to the largest sets of tuple. By this, we may take the results formed for different use e.g. Patient investigation, frequency of different disease.

Keywords


Knowledge Discovery, Medical Database, Association Rule Mining Techniques, Analysis, Transformation.