Kernel Smoothing for ROC Curve and Estimation of its Area 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 ability to separate positive from negative cases. It is especially useful in evaluating predictive models and in comparing with 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 is used. The Area Under ROC Curve (AUC) is frequently 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-patients in congenital hypothyroidism screening in Isfahan province. Using this method, TSH ROC curves from infants in Isfahan were fitted. For evaluating of accuracy of this test, AUC and its standard error calculated. Also effectiveness of the kernel methods in comparison with other methods are showed.