Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/303404
Title: Performance enhancement of data mining algorithms for chronic diseases and medline documents
Researcher: Nirmala Devi M
Guide(s): Appavu Alias Balamurugan S
Keywords: Engineering and Technology
Computer Science
Computer Science Software Engineering
Data mining algorithms
Chronic diseases
MEDLINE
University: Anna University
Completed Date: 2019
Abstract: Medical Data mining is the process of extracting hidden patterns from large medical data This thesis aims to develop a disease prediction model with enhanced performance and MEDLINE document classification model to improve the retrieval of the disease related information Different models are applied to the prediction of clinical data To design a disease prediction model an Amalgam kNN approach is proposed This classification model is proposed for chronic communicable as well as non communicable diseases datasets The datasets are collected from the University of California Irvine UCI Machine Learning repository GitHub and Kaggle data science repository In the Amalgam kNN model high dimensional data are handled by Correlation based Feature Selection CFS attribute reduction method and the kMeans algorithm identifies natural groupings among the observations To build the classifier kNN classification algorithm is used The proposed model produces one of the best Classification Accuracy of 97 4 for the PIDD data set and 99 7 for High Dimensional Diabetic readmission dataset and it is compared with simple kNN and other leading disease prediction models The proposed model is validated for all the chronic disease datasets and the results are compared with the leading clustering and classification algorithms It achieves better performance than Decision Tree J48 Support Vector Machine SVM Neural Network NN and Naive Bayes NB classification algorithms newline
Pagination: xviii,157p.
URI: http://hdl.handle.net/10603/303404
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificates.pdf427.01 kBAdobe PDFView/Open
03_abstracts.pdf129.96 kBAdobe PDFView/Open
04_acknowledgements.pdf5.3 kBAdobe PDFView/Open
05_contents.pdf16.23 kBAdobe PDFView/Open
06_list_of_tables.pdf50.62 kBAdobe PDFView/Open
07_list_of_figures.pdf6.34 kBAdobe PDFView/Open
08_list_of_abbreviations.pdf112.49 kBAdobe PDFView/Open
09_chapter1.pdf237.43 kBAdobe PDFView/Open
10_chapter2.pdf295.8 kBAdobe PDFView/Open
11_chapter3.pdf838.41 kBAdobe PDFView/Open
12_chapter4.pdf857.84 kBAdobe PDFView/Open
13_conclusion.pdf165.57 kBAdobe PDFView/Open
14_references.pdf177.25 kBAdobe PDFView/Open
15_list_of_publications.pdf213.71 kBAdobe PDFView/Open
80_recommendation.pdf139.63 kBAdobe PDFView/Open
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