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We encoded all independent and dependent variables as binary values (Supplementary Data 1) and built a logistic regression model for each of 18 side effect classes.
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.
Next we built a logistic regression model to forecast whether or not there will be a CRI event in the future.
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.
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The approach first builds a logistic regression learner for each source domain.
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.
In previous work,40 we built and compared a logistic regression classifier with the random forest classifier.
Working with data provided by WalMart, the team built a multiclass logistic regression model to predict the most popular product at a particular store on a given day.
More suggestions(14)
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built a multivariable regression
built a normal regression
built a binary regression
built a multiple regression
built a logistic model
built a linear regression
built a hierarchical regression
built a multivariate regression
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