Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/519931
Title: Enhancing disease prediction accuracy in the healthcare industry using SVM based machine learning techniques
Researcher: Karthikeyan, H
Guide(s): Menakadevi, T
Keywords: Computer Science
Computer Science Information Systems
Data analytics
Decision Support System
Engineering and Technology
Machine Learning algorithms
University: Anna University
Completed Date: 2022
Abstract: Data 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
Pagination: xviii, 118p.
URI: http://hdl.handle.net/10603/519931
Appears in Departments:Faculty of Information and Communication Engineering

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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
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