Title page for etd-0708111-161254


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URN etd-0708111-161254
Author Li-wen Kao
Author's Email Address No Public.
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Department Applied Mathematics
Year 2010
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Comparison of Discrimination between Logistic Model with Distance Indicator and Regularized Function for Cardiology Ultrasound in Left Ventricle
Date of Defense 2011-06-09
Page Count 42
Keyword
  • factor score
  • ROC curve
  • systole and diastole
  • gray-scale value
  • factor analysis
  • quadratic discriminant analysis
  • linear discriminant analysis
  • Abstract Most of the cardiac structural abnormalities will be examined by echocardiography. With more understanding of heart diseases, it is commonly recognized that heart failures are closely related to left ventricular systolic and diastolic functions. This work discusses the association between gray-scale differences and the risk of heart disease from the changes in left ventricular systole and diastole of ultrasound image. Owing to the large dimension
    of data matrix, following Chen (2011), we also simplify the influence factors by factor analysis and calculate factor scores to present the characteristics of subjects.
    Two kinds of classification criteria are used in this work, namely logistic model with distance indicator and discriminant function. According to Guo et al. (2001), we calculate the Mahalanobis distance from each subject to the center of normal and abnormal group, then use logistic model to fit the distances for classification later. This is called logistic model with distance indicator. For the discriminant analysis, the regularized method by Friedman (1989) for estimation of covariance matrix is used, which is more flexible and can improve the covariance matrix estimates when the sample size is small. As far as the
    cut-point of ROC curve, following the approach as in Hanley et al. (1982), we find the most appropriate cut-point which has good performances for both sensitivity and specificity under the same classification criteria. Then the regularized method and the cut-point of ROC curve are combined to be a new classification criterion. The results under the new
    classification criterion are presented to classify normal and abnormal groups.
    Advisory Committee
  • Mei-Hui Guo - chair
  • Chung Chang - co-chair
  • Fu-Chuen Chang - co-chair
  • May-Ru Chen - co-chair
  • Mong-Na Lo Huang - advisor
  • Kai-Hsien Hsieh - advisor
  • Files
  • etd-0708111-161254.pdf
  • indicate not accessible
    Date of Submission 2011-07-08

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