logistic regression models are frequently used in clinicalresearch and particularly for modeling disease status and patientsurvival. in practice, clinical studies have several limitationsfor instance, in the study of rare diseases or due ethical considerations, we can only have small sample sizes. in addition, the lack of suitable andadvanced measuring instruments lead to non-precise observatio...

Serial endomyocardial biopsies (EMBs) are scheduled even several years after heart transplantation (HTx) to monitor for late rejection (LR). However, repeated EMBs are associated with an increased risk for fatal complications and decrease the quality of life of the recipient. We retrospectively analyzed clinical data from 42 adult recipients who had received HTx and were followed > 1 year at th...

We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods – it estimates class probabilities as well as providing a simple classification – and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classifi...

We would like to submit the following correction to the published paper [1], the reason for this action is that the data in Table 3 were reanalyzed by one more accurate statistic method: On page 12, the sentence of paragraph three "OR and 95% CI were calculated by limited backward-LR (likelihood ratio) logistic regression analysis with adjustment by clinical variables" should be corrected into ...

BACKGROUND Despite low specificity, serum prostate-specific antigen (PSA) is widely used in screening for prostate cancer. Specificity can be improved by measuring free and total PSA and by combining these results with clinical findings. Methods such as neural networks and logistic regression are alternatives to multistep algorithms for clinical use of the combined findings. METHODS We compar...

Different prediction methods give different performance predictions when used for daily fresh food sales forecasting. Logistic Regression (LR) is a good choice for binary data, the Moving Average (MA) method is good for simple prediction, while the Back-Propagation Neural Network (BPNN) method is good for long term data. In this study we develop and compare the performance of three sales foreca...

Accidental or intentional contamination in a water distribution system (WDS) has recently attracted attention due to the potential hazard to public health and the complexity of the contaminant characteristics. The accurate and rapid characterization of contaminant sources is necessary to successfully mitigate the threat in the event of contamination. The uncertainty surrounding the contaminants...

Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non...

This paper describes a new method for classification of hyperspectral images for extracting carthographic objects. The developed method is intended as a tool for automatic map updating. The idea is to use an existing map of the region of interest as a learning set. The proposed method is based on logistic regression. Logistic regression (LR) is a supervised multi-variate statistical tool that f...