slugData Mining Techniques on Medical Data for Finding Locally Frequent Diseases

Abstract

In the last decade there has been increasing usage of data mining techniques on medical data for discovering useful patterns or trends which are used in decision making and diagnosis. Data mining techniques such as clustering, association rule mining, classification, regression, CART (Classification and Regression Tree) are extensively used in healthcare domain. Data mining algorithms, when aptly used, are capable of improving the quality of prediction, diagnosis and disease classification. The main aim of this paper is to analyze data mining techniques needed for medical data mining especially to find out the locally frequent diseases such as heart ailments, lung cancer, breast cancer and so on. We evaluate the data mining techniques for finding locally frequent patterns in terms of accuracy, cost, performance, and speed. We also compare data mining techniques with conventional methods.

Authors and Affiliations

Shashi Chhikara, Purushottam Sharma

Keywords

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  • EP ID EP18148
  • DOI -
  • Views 318
  • Downloads 8

How To Cite

Shashi Chhikara, Purushottam Sharma (2014). slugData Mining Techniques on Medical Data for Finding Locally Frequent Diseases. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2(5), -. https://europub.co.uk/articles/-A-18148