Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/519974
Full metadata record
DC FieldValueLanguage
dc.coverage.spatialEnhancing disease prediction accuracy in the healthcare industry using SVM based machine learning techniques
dc.date.accessioned2023-10-22T06:20:27Z-
dc.date.available2023-10-22T06:20:27Z-
dc.identifier.urihttp://hdl.handle.net/10603/519974-
dc.description.abstractData analytics is the process of identifying new insights from the newlinedata. Data analytics in the healthcare industry helps identify new patterns in newlinethe disease, provide personalized healthcare facilities, and diagnose and newlinepredict the disease. newlineIn developing countries like India, healthcare data analytics are newlinehelpful for remote people to access the medical facilities efficiently and newlinereduce human errors during disease diagnosis using Machine Learning newlinealgorithms. It improves the disease prediction accuracy by selecting newlineappropriate Machine Learning algorithms in the healthcare data analytics. newlineA Decision Support System (DSS) is proposed to predict and improve the newlineprediction accuracy using Machine Learning algorithms. This thesis addresses newlinethe need for DSS to predict the disease, identify optimal features, and newlineoptimise the predictive algorithm to improve the disease prediction accuracy newlinein the healthcare industry. newlineThe central argument of this thesis is to understand the working newlineprocedure of various machine learning algorithms and to employ different newlinefeature selection techniques in the healthcare industry. The research work newlinefocuses on the design of DSS to predict diseases in the healthcare industry newlineand exhibit the opportunities to improve the prediction accuracy at the feature newlineselection level and predictive model levels. A Feature Selection model and newlineSVM based Improved Radial-Bias techniques were proposed in this research newlinework. newline
dc.format.extentxviii, 117p.
dc.languageEnglish
dc.relationp.107-117
dc.rightsuniversity
dc.titleEnhancing disease prediction accuracy in the healthcare industry using SVM based machine learning techniques
dc.title.alternative
dc.creator.researcherKarthikeyan, H
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordEngineering and Technology
dc.subject.keywordHealthcare Industry
dc.subject.keywordMachine learning
dc.subject.keywordSVM
dc.description.note
dc.contributor.guideMenakadevi, T
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2022
dc.date.awarded2022
dc.format.dimensions21 c m
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File73.38 kBAdobe PDFView/Open
02_prelim pages.pdf3.06 MBAdobe PDFView/Open
03_content.pdf99 kBAdobe PDFView/Open
04_abstract.pdf65.4 kBAdobe PDFView/Open
05_chapter 1.pdf1.87 MBAdobe PDFView/Open
06_chapter 2.pdf8.81 MBAdobe PDFView/Open
07_chapter 3.pdf3.61 MBAdobe PDFView/Open
08_chapter 4.pdf4.09 MBAdobe PDFView/Open
09_chapter 5.pdf902.02 kBAdobe PDFView/Open
10_chapter 6.pdf878.11 kBAdobe PDFView/Open
11_annexures.pdf121.55 kBAdobe PDFView/Open
80_recommendation.pdf117.66 kBAdobe PDFView/Open


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: