Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/409134
Title: A Hybrid Classifier Based on Kernel Extreme Learning Machine for Heart Disease Prediction
Researcher: Sharmila, S
Guide(s): Indra Gandhi, M.P
Keywords: Computer Science
Computer Science Artificial Intelligence
Engineering and Technology
University: Mother Teresa Womens University
Completed Date: 2022
Abstract: Data Mining is a process to extract the useful information from a huge data repository. It refers to mine the information from the large data set. Data mining technique is a one of the analytical tool to predict the heart disease. It is used to maintain the electronic data and it can reduce the manual task. Data mining is performed by continuous exploration of patterns and the methodical correlations between the patterns and the respective applications. On making usage of the authentic patterns, that was identified and applied the outcomes of the analysis to the fresh group of data is gathered. Fundamental motivation of data mining provides the prediction. Data mining provides the estimation from the patterns which are maximum utilized category of data mining. It can be employed to a lot of areas. newline
Pagination: xv, 153p.
URI: http://hdl.handle.net/10603/409134
Appears in Departments:Department of Computer Science

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01_title.pdfAttached File241 kBAdobe PDFView/Open
02_declaration.pdf442.69 kBAdobe PDFView/Open
03_certificate.pdf306.86 kBAdobe PDFView/Open
04_acknowledgement.pdf241.19 kBAdobe PDFView/Open
05_contents.pdf121.83 kBAdobe PDFView/Open
06_list of tables.pdf152.38 kBAdobe PDFView/Open
07_abstract.pdf263.03 kBAdobe PDFView/Open
08_chapter 1.pdf909.47 kBAdobe PDFView/Open
09_chapter 2.pdf678.42 kBAdobe PDFView/Open
10_chapter 3.pdf418.38 kBAdobe PDFView/Open
11_chapter 4.pdf466.33 kBAdobe PDFView/Open
12_chapter 5.pdf954.95 kBAdobe PDFView/Open
13_chapter 6.pdf915.17 kBAdobe PDFView/Open
14_chapter 7.pdf145.06 kBAdobe PDFView/Open
15_summary.pdf140.28 kBAdobe PDFView/Open
16_bibliography.pdf520.69 kBAdobe PDFView/Open
17_abbreviations.pdf170.25 kBAdobe PDFView/Open
80_recommendation.pdf248.38 kBAdobe PDFView/Open
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