THE ROLE AND PLACE OF LOGISTIC REGRESSION AND ROC ANALYSIS IN SOLVING MEDICAL DIAGNOSTIC TASK

Abstract

Diagnostics, equally with prevention and treatment, is a basis of medical science and practice. For its history the medicine has accumulated a great variety of diagnostic methods for different diseases and pathologic conditions. Nevertheless, new tests, methods and tools are being developed and recommended to application nowadays. Such indicators as sensitivity and specificity which are defined on the basis of fourfold contingency tables construction or ROC-analysis method with ROC – curve modelling (Receiver operating characteristic) are used as the methods to estimate the diagnostic capability. Fourfold table is used with the purpose to estimate the method which confirms or denies the diagnosis, i.e. a quality indicator. ROC-curve, being a graph, allows making the estimation of model quality by subdivision of two classes on the basis of identifying the point of cutting off a continuous or discrete quantitative attribute.The method of logistic regression technique is introduced as a tool to develop some mathematical-statistical forecasting model of probability of the event the researcher is interested in if there are two possible variants of the outcome. The method of ROC-analysis is chosen and described in detail as a tool to estimate the model quality. The capabilities of the named methods are demonstrated by a real example of creation and efficiency estimation (sensitivity and specificity) of a forecasting model of probability of complication development in the form of pyodermatitis in children with atopic dermatitis.

Keywords

• математико-статистическая модель
• логистическая регрессия
• roc-анализ
• чувствительность диагностического метода
• специфичность диагностического метода
• атопический дерматит