Logistic regression model with TreeNet and association rules analysis: applications with medical datasets

Abstract

This study establishes an innovative and effective approach for generating new variables and interactions for logistic regression using the two data mining techniques TreeNet and association rules analysis. With TreeNet as the first step in our logistic model building, the new variables are generated by discretizing the quantitative variables. With ASA as the following step, the new interactions are generated from all the original categorical variables and all the newly generated predictors from TreeNet. These newly generated variables and interactions (low- and high-order) are used as candidate predictors to build an optimal logistic regression model. The method is tested on and the results given for four medical datasets—heart disease, heart failure, breast cancer, and hepatitis—with the complete model process presented for the last of these. The results indicate that building a model in this way constitutes a major advance in logistic regression modeling that cannot be achieved using other existing methods.

Keywords:

• Association rules analysis
• Hepatitis
• Interaction effect
• Logistic regression model
• Model selection
• TreeNet