

Logistic Regression
For comparison, we also built a logistic regression model using stepwise regression. Predictive validation of this model on the same independent set produced 10 errors in the nonstroke subjects (false positive rate = 0.09) and 3 errors in the stroke subjects (false negative rate = 0.43), with an overall accuracy of 88%, compared to the 98.2% of the Bayesian network model.
The two box plots on the right show, side by side, the difference in predictive discrimination between stroke (red) and nonstroke (blue) subjects using the Bayesian network model (above) and logistic regression (below). The greater overlapping of the two distributions in the box plot below compared to the distributions above shows a significantly decreased discriminatory power of the logistic regression model, consistent with its lower predictive accuracy. 


CoefficientsBox Plot
