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In our study since none of the socio-demographic variables were significant at the univariate analysis we could not build a logistic regression model.
We demonstrated a novel approach that combines public and private data sets to build a logistic regression model in a distributed manner.
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The approach first builds a logistic regression learner for each source domain.
Next we built a logistic regression model to forecast whether or not there will be a CRI event in the future.
We studied patients who underwent extracorporeal shock wave lithotripsy (SWL) to investigate the factors influencing the outcome, and built a logistic regression model to estimate the stone-free rate (SFR) after SWL.
We built a logistic regression model using the same features in Pan et al. (see Methods).
We built a logistic regression model for Chinese Han population in Taiwan based on the data of these 6 alleles and gender, and the area under curve of the receiver operating characteristic (ROC) curve was 0.75 (Figure 2).
We built a logistic regression model to predict cell type (ES vs non-ES) with three variables: antibody fluorescence, cell granularity measured by side scatter values, and the plate in which the clone was located.
A previous study conducted pathway analysis on the same data set [ 23] and built a logistic regression model of 277 genes with a variable selection algorithm.
To better understand the basis for susceptibility to YRV, we built a logistic regression model to identify the properties of genes that are predictive of being in the YRV class.
For children aged 0 4 at the first time of having asthma-like symptoms in the primary care setting, Caudri et al. [ 27] in 2009 built a logistic regression model to predict asthma development at age 7 8.
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