Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/252043
Title: Efficient data mining techniques for medical data
Researcher: Saleema, J S
Guide(s): Shenoy, P Deepa
Keywords: Engineering and Technology,Computer Science,Computer Science Interdisciplinary Applications
University: CHRIST University
Completed Date: 2018
Abstract: Healthy decision making for the well being is a challenge in the current era with abundant information everywhere. Data mining, machine newlinelearning and computational statistics are the leading fields of study that are supporting the empowered individual to take valuable decisions to optimize the outcome of any working domain. High demand for data newlinehandling exists in healthcare, as the rate of increase in patients is proportional to the rate of population growth and life style changes. Techniques for early diagnosis and prognosis prediction of diseases are the need of the hour to provide better treatment for the human community. Data mining techniques are a boon for building a quality and newlineefficient model for health prediction applications. As cancer explodes everywhere in recent years, the data sets from cancer newlineregistries have been focused as the medical data in this research. The main aim of thesis is to build a constructive and efficient classifier model for cancer prognosis prediction. Most of the existing system develops a diagnosis prediction models from the screening or survey data, as the data newlineset is widely available and are easy to collect due the insensitive nature of newlinethe factors involved in such research. Whereas the prognosis prediction requires a sensitive details of the patients those who are under treatment for a diagnosed disease. Hospitals and the community registries newlinemaintained by the government are the main source for data collection. Well maintained electronic hospital records with histopathology information is not public in India for the researchers. Hence cancer data newlinefrom a US based open access data center has been used in this research for all experimentation. This research work is a progressive model that gradually improves the newlineprediction accuracy by selecting appropriate data mining techniques in each phase. newline
Pagination: A4
URI: http://hdl.handle.net/10603/252043
Appears in Departments:Department of Computer Science

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File36.75 kBAdobe PDFView/Open
02_declaration.pdf195.8 kBAdobe PDFView/Open
03_certificate.pdf396.51 kBAdobe PDFView/Open
04_acknowledgement.pdf51.96 kBAdobe PDFView/Open
05_abstract.pdf10.17 kBAdobe PDFView/Open
06_contents.pdf23.11 kBAdobe PDFView/Open
07_list_of_tables.pdf220.6 kBAdobe PDFView/Open
08_list_of_figures.pdf10.83 kBAdobe PDFView/Open
09_abbreviations.pdf9.11 kBAdobe PDFView/Open
10_chapter1.pdf259.75 kBAdobe PDFView/Open
11_chapter2.pdf154.75 kBAdobe PDFView/Open
12_chapter3.pdf523.47 kBAdobe PDFView/Open
13_chapter4.pdf504.69 kBAdobe PDFView/Open
14_chapter5.pdf699.29 kBAdobe PDFView/Open
15_chapter6.pdf402.85 kBAdobe PDFView/Open
16_chapter7.pdf512.77 kBAdobe PDFView/Open
17_conclusion.pdf26.65 kBAdobe PDFView/Open
18_bibliography.pdf136.04 kBAdobe PDFView/Open
19_publication.pdf8.37 kBAdobe PDFView/Open
20_appendix.pdf460.33 kBAdobe PDFView/Open


Items in Shodhganga are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetric Badge: