Logistic regression analysis prediction

Specific predictive factors for outcome after surgical intervention have not been well defined in the literature. In one prospective, multicenter observational study of 95 patients, the state of consciousness was the only predictive factor retained in a logistic regression analysis." In this study, there was a 2.8-fold increased risk for poor outcome for each increase on a three-step scale (awake/drowsy, somnolent/ stuporous, and comatose), and good outcomes (modified Rankin Scale score <2) were achieved in 86%, 76%, and 47% of patients within each group, respectively. 

Multiple logistic regression analysis A statistical model used to predict the probability of the occurrence of an event using several predictor variables. 

Fig. 17.8 Schematic representation of the PK/PD model. C = model predicted drug concentrations in plasma R = the free form of the calcitonine gene-related peptide (CGRP) receptor R = the blocked form of the CGRP receptor, which has been related to the severity of headache and time to rescue medication using logistic regression and time-to-event analysis.

Arena VC, Sussman NB, Mazumdar S, et al. The utility of structure-activity relationship (SAR) models for prediction and covariate selection in developmental toxicity Comparative analysis of logistic regression and decision tree models. SAR QSAR Environ Res. 2004 15(1) 1-18. 

These classification methods use different principles and rules for learning and prediction of class membership, but wiU usually produce a comparable result. Some comparisons of the methods have been given (i.e., Kotsiantis, 2007 Rani et al., 2006). Although the modem methods such as SVM have demonstrated very good performance, the drawback is that the model becomes an incomprehensible black-box that removes the explanatory information provided by, for example, a logistic regression model. However, classification performance usually outweighs the need for a comprehensible model. PCA has been used for classification based on bioimpedance measurements. Technically, PCA is not a method for classification but rather a method of data reduction, more suitable as a parameterization step before the classification analysis. 

Also the results of the analysis of deviance and the likelihood ratio test of the significance of the regression coefficients were positive for this model. Thus we can say that our model, created by the multidimensional logistic regression, is suitable to predict the morbidity of the patients who undergo the open surgeries of colon in The Faculty Hospital Ostrava. 

The logistic regression model was designed to examine how functionality of the gas supply system elements affect energy system criticality value by using probability scores as the predicted values of the dependent variable of gas supply system. The purpose of this statistical analysis is to determine which elements of gas supply system influence high value of system criticality (statistical classification model) (Ozdemir, 2011 Flahaut, 2004 Fang et al, 2012). 

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