A rough set-based multiple criteria linear programming approach for the medical diagnosis and prognosis

  • Authors:

  • Zhiwang Zhang;Yong Shi;Guangxia Gao

  • Affiliations:

  • School of Information of Graduate University of Chinese Academy of Sciences, China and Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, No. 80 Zhongguancun East ...;Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, No. 80 Zhongguancun East Road, Beijing 100080, China and College of Information Science and Technology, Univers ...;Foreign Language Department, Shandong Institute of Business and Technology, Yantai, Shandong 264005, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

It is well known that data mining is a process of discovering unknown, hidden information from a large amount of data, extracting valuable information, and using the information to make important business decisions. And data mining has been developed into a new information technology, including regression, decision tree, neural network, fuzzy set, rough set, and support vector machine. This paper puts forward a rough set-based multiple criteria linear programming (RS-MCLP) approach for solving classification problems in data mining. Firstly, we describe the basic theory and models of rough set and multiple criteria linear programming (MCLP) and analyse their characteristics and advantages in practical applications. Secondly, detailed analysis about their deficiencies are provided, respectively. However, because of the existing mutual complementarities between them, we put forward and build the RS-MCLP methods and models which sufficiently integrate their virtues and overcome the adverse factors simultaneously. In addition, we also develop and implement these algorithm and models in SAS and Windows system platforms. Finally, many experiments show that the RS-MCLP approach is prior to single MCLP model and other traditional classification methods in data mining, and remarkably improve the accuracy of medical diagnosis and prognosis simultaneously.