Framework for integration of domain knowledge into logistic regression

Traditionally, machine learning extracts knowledge solely based on data. However, huge volume of knowledge is available in other sources which can be included into machine learning models. Still, domain knowledge is rarely used in machine learning. We propose a framework that integrates domain knowledge in form of hierarchies into machine learning models, namely logistic regression. Integration of the hierarchies is done by using stacking (stacked generalization). We show that the proposed framework yields better results compared to standard logistic regression model. The framework is tested on the binary classification problem for predicting 30-days hospital readmission. Results suggest that the proposed framework improves AUC (area under the curve) compared to logistic regression models unaware of domain knowledge by 9% on average.