Kernel smoothing for ROC curve and estimation for thyroid stimulating hormone
Receiver Operating Characteristic (ROC) Curves are frequently used in biomedical informatics research to evaluate classification and prediction models to support decision, diagnosis, and prognosis. ROC analysis investigates the accuracy of models and has ability to separate positive from negat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Kebangsaan Malaysia
2011
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| Online Access: | http://journalarticle.ukm.my/3560/ http://journalarticle.ukm.my/3560/ http://journalarticle.ukm.my/3560/1/special%2520issue%25202011_33.pdf |
| Summary: | Receiver Operating Characteristic (ROC) Curves
are frequently used in biomedical informatics
research to evaluate classification and prediction
models to support decision, diagnosis, and
prognosis. ROC analysis investigates the accuracy
of models and has ability to separate positive from
negative cases. It is especially useful in evaluating
predictive models and compare to other tests
which produce output values in a continuous
range. Empirical ROC curve is jagged but a true
ROC curve is smooth. For this purpose kernel
smoothing were used. The Area Under ROC Curve
(AUC) frequently is used as a measure of the
effectiveness of diagnostic markers. In this study
we compare estimation of this area based on
normal assumptions and kernel smoothing. This
study used measurements of TSH from patients
and non-diseased people of congenital
hypothyroidism screening in Isfahan province.
Using the method, TSH ROC curves from
Isfahani's infants were fitted. For evaluating of
accuracy of this test, AUC and its standard error
calculated. Also effectiveness of the kernel
methods in comparison to other methods showed. |
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