Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/24396
Title: Improved hybrid models for automated classification of cardiotocogram data
Researcher: Sundar, C
Guide(s): Geetharamani, G
Keywords: Cardiotocography
Fetal heart rate
Information and communication engineering
Outlier Based Bi-level Neural Network
Outlier Based Bi-Model Neural Network
Tocographic
Upload Date: 2-Sep-2014
University: Anna University
Completed Date: 01/11/2013
Abstract: The major challenges in medical domain is the extraction of comprehensible knowledge from medical diagnosis such as Cardiotocography In this information era, the use of machine learning tools in medical diagnosis is increasing gradually This is mainly because the effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases Cardiotocography consisting of fetal heart rate and tocographic measurements is used to evaluate fetal well being during the delivery Since 1970 many researchers have employed different methods to help the doctors to interpret the CTG trace pattern from the field of signal processing and computer programming They have supported doctors with interpretations in order to reach a satisfactory level of reliability so as to act as a decision support system in obstetrics More than 30 years after the introduction of antepartum Cardiotocography into clinical practice the predictive capacity of the method remains controversial In a review of lot of articles published on this subject it was found that its reported sensitivity newlinevaries between 2 and 100 and its specificity between 37 and 100 So in this work machine learning and datamining techniques are used for the classification of CTG data and propose new methods with improved classification accuracy newline newline
Pagination: xix, 194p.
URI: http://hdl.handle.net/10603/24396
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificate.pdf772.65 kBAdobe PDFView/Open
03_abstract.pdf8.51 kBAdobe PDFView/Open
04_acknowledgement.pdf6.77 kBAdobe PDFView/Open
05_contents.pdf26.33 kBAdobe PDFView/Open
06_chapter1.pdf16.1 kBAdobe PDFView/Open
07_chapter2.pdf3.7 MBAdobe PDFView/Open
08_chapter3.pdf48.45 kBAdobe PDFView/Open
09_chapter4.pdf715.65 kBAdobe PDFView/Open
10_chapter5.pdf532.11 kBAdobe PDFView/Open
11_chapter6.pdf426 kBAdobe PDFView/Open
12_chapter7.pdf206.71 kBAdobe PDFView/Open
13_chapter8.pdf268.07 kBAdobe PDFView/Open
14_chapter9.pdf7.75 kBAdobe PDFView/Open
15_references.pdf29.75 kBAdobe PDFView/Open
16_publications.pdf7.99 kBAdobe PDFView/Open
17_vitae.pdf5.75 kBAdobe PDFView/Open


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