Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/330508
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dc.coverage.spatialComputer Science
dc.date.accessioned2021-07-07T10:17:58Z-
dc.date.available2021-07-07T10:17:58Z-
dc.identifier.urihttp://hdl.handle.net/10603/330508-
dc.description.abstractnewline Medical diagnosis of diseases is complex in nature. Computerized diagnostic tools have received significant attention over the past few decades to assist medical practitioners in the diagnosis of diseases. Medical decision support systems are one of the main applications of machine learning techniques. The physician uses his knowledge in the subjects, expertise and talent in order to diagnose the disease. A diagnostic procedure starts with the patient s complaints and the doctor learns more about patient s situation. Hypertension is the major risk factor for other diseases, thus it is a domain requiring attention, in health care system. In order to decrease the risk of hypertension early screening of patients before they get its symptoms can be an effective solution. Hypertension is a most serious disease that affects a wide range of the population, especially the elderly after the age of 50. Individuals with the age of more than 50 have 90% lifetime risk of developing hypertension. Accurate measurement of blood pressure is essential in the diagnosis and treatment of hypertension. The amount of data coming from clinical analysis of the diseases is quite large.
dc.format.extent181p.
dc.languageEnglish
dc.relation152 Nos.
dc.rightsuniversity
dc.titleStudy on Fusion Methodology for Diagnosis of Hypertension Using Neural Networks
dc.title.alternative
dc.creator.researcherSumathi, B
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Artificial Intelligence
dc.subject.keywordEngineering and Technology
dc.description.noteBibliography: p.183 201
dc.contributor.guideSanthakumaran, A
dc.publisher.placeKodaikanal
dc.publisher.universityMother Teresa Womens University
dc.publisher.institutionDepartment of Computer Science
dc.date.registered2007
dc.date.completed2017
dc.date.awarded2018
dc.format.dimensionsA4
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Computer Science

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04_chapter 1.pdf709.89 kBAdobe PDFView/Open
05_chapter 2.pdf245.59 kBAdobe PDFView/Open
06_chapter 3.pdf677.17 kBAdobe PDFView/Open
07_chapter 4.pdf731.78 kBAdobe PDFView/Open
08_chapter 5.pdf604.59 kBAdobe PDFView/Open
09_chapter 6.pdf414.51 kBAdobe PDFView/Open
10_chapter 7.pdf677.6 kBAdobe PDFView/Open
80_recommendation.pdf123 kBAdobe PDFView/Open


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