Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/203227
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dc.coverage.spatialDATA MINING
dc.date.accessioned2018-05-07T09:04:10Z-
dc.date.available2018-05-07T09:04:10Z-
dc.identifier.urihttp://hdl.handle.net/10603/203227-
dc.description.abstractCardiovascular diseases (CVD) are becoming common with changing life styles across the world. It majorly happens due to various disorders of heart and blood vessels, and is considered as one of the foremost reason of death and disability. Early disease diagnosis and treatment can reduce the threat of having further severity of the disease, and hence, associated mortality. With the availability of the clinical data of probable and potential patients, it becomes imperative to find suitable models to diagnose these diseases both economically and accurately. One of the most common CVD is Coronary Artery Disease (CAD) that infests in presence of atherosclerotic plaques in coronary arteries restricting the flow of blood to the heart muscle by physically clogging the artery, leading to cardiac arrest or myocardial infarction. CAD patients can be diagnosed accurately using angiography which is an invasive, costly and highly technical procedure. Angiography can be risky, may lead to further aggravation of the disease and is not suitable for screening of larger population or for a follow-up of a subject immediately after treatment or surgery. Due to the limitations of CAD diagnostic methods, researchers are seeking other methods that are less expensive, less risky, less complex, fast and easily reproducible. This work explores various data mining techniques to model diagnosis of CAD and its severity. newlineThe benchmarked Cleveland heart disease data from UCI machine learning repository and CAD clinical data from the Department of Cardiology, Indira Gandhi Medical College, India were utilized for the experimental purposes. Data set consist of 26 features. newline newlineA hybrid data mining frame-work is developed for detection of CAD using only noninvasive clinical parameters. Risk factors are identified using correlation based feature subset selection with particle swam optimization (PSO) search method. A K-means clustering algorithm is applied before the model construction. Supervised learning algorithms such as multinomial logistic regression (MLR), multi-layer perceptron (MLP), C4.5 and fuzzy unordered rule induction (FURIA) are then used to model CAD cases. The hybridization improves accuracy of algorithms by 8.3% to 11.4% for the Cleveland data set. Furthermore, another novel cluster based method is used for missing value imputation and then, a fuzzy rule based classification newlinesystem is explored to model the severity of CAD cases. Significant diagnostic accuracy is achieved by fuzzy rule based classification system. These methods can serve as an adjunct tool to screen CAD cases without using invasive clinical diagnosis and help a medical practitioner to prescribe appropriate treatments, as the case may be. newline newline
dc.format.extent92 p.
dc.languageEnglish
dc.relationI EEE
dc.rightsuniversity
dc.titleDATA MINING MODELS FOR CAD USING NON INVASIVE CLINICAL PARAMETERS
dc.title.alternative
dc.creator.researcherLuxmi Verma
dc.subject.keywordDATA MINING, CORONARY ARTERY DISEASE, ANGIOGRAPHY
dc.description.note(References p-77-91, Appendix p.73-76)
dc.contributor.guideSangeet Srivastava
dc.publisher.placeGurgaon
dc.publisher.universityThe Northcap University (Formerly ITM University, Gurgaon)
dc.publisher.institutionDepartment of CSE and IT
dc.date.registered2-8-2010
dc.date.completed2017
dc.date.awarded30/07/2017
dc.format.dimensions
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of CSE & IT

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02 certificate.pdfAttached File162.16 kBAdobe PDFView/Open
03certificate from the student.pdf84.26 kBAdobe PDFView/Open
04table of contents.pdf96.54 kBAdobe PDFView/Open
05list of tables.pdf91.57 kBAdobe PDFView/Open
06list of figures.pdf86.62 kBAdobe PDFView/Open
07list of abbreviations.pdf90.87 kBAdobe PDFView/Open
08abstract.pdf85.38 kBAdobe PDFView/Open
09_chapter 1.pdf230.53 kBAdobe PDFView/Open
10_chapter2.pdf380.76 kBAdobe PDFView/Open
11_chapter3.pdf789.44 kBAdobe PDFView/Open
12_chapter4.pdf615.02 kBAdobe PDFView/Open
13_chapter5.pdf556.01 kBAdobe PDFView/Open
14_chapter6.pdf93.98 kBAdobe PDFView/Open
15_appendix a &b.pdf207.17 kBAdobe PDFView/Open
16_references.pdf314.81 kBAdobe PDFView/Open
17_publications.pdf188.37 kBAdobe PDFView/Open


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