Applying the Logistic Regression Model to Predict the Stenosis in Carotid Artery Using the Sequential Color Doppler Ultrasound Image Processing

Document Type : Original Article


Background- Early detection of stenosis in carotid artery is essential because it directly affects the
patients' clinical management and is of prognostic value. Therefore, estimating mechanical
properties of this artery in normal and atherosclerosis cases is important as far as medical
treatment is concerned. We applied a logistic regression model to predict carotid artery
stenosis in a group of patients based on the quantitative features extracted from the processing
of the conventional color Doppler ultrasound images.
Methods- Our database includes 128 patient records consisting 10 quantitative features. The
database is then randomly divided into the training and validation samples including 98 and
30 patient records respectively. The training and validation samples are used to construct the
logistic regression model and to validate its performance. Finally, important criteria such as
sensitivity, specificity, accuracy and receiver operating characteristic curve (ROC) analysis
for this method are evaluated.
Results- Our results show that the logistic regression model is able to classify correctly 28 out of 30
cases presented in the validation sample. The output of this method showed a high positive
predictive value of 94%.
Conclusion- We have established a logistic discriminator approach which is able to predict the
probability of stenosis in the carotid artery using features extracted from ultrasonic
measurements on ultrasound imaging (Iranian Heart Journal 2008; 9 (2):43-50).


M. Mokhtari-Dizaji PhD, P. Abdolmaleki2 PhD, H. Saberi3 MD, T. Rahmani1 MSc

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