Predictive and preventive models for diabetes prevention using clinical information in electronic health record

In this work, we constructed diabetes predictive models using electronic health record data, which could potentially have better preventive power than other diabetes predictive models known according to our knowledge. Diabetes is one of the most common, costly and complicated diseases all over the world, including China. To tackle the complexity of diabetes, electronic health record has been widely used to support physicians in integrated care. However, diabetes predictive models using electronic health record may lack of preventive power when the clinical measurements directly related to diabetes diagnosis criteria are used. To overcome this limitation, we did not use glucose, insulin, C-peptide and HbA1C clinical measurements in classifying diabetes patients. We used decision-table and support vector machine algorithm to build predictive models. As the result, our decision-table-based model achieves accuracy of 0.879, AUC of 0.921, precision of 0.898 and recall of 0.904, which is comparable with any known definition to diabetes. Our support-vector-machine-based model achieves accuracy of 0.660, AUC of 0.584, precision of 0.652 and recall of 0.939. We also found 37 measurements significantly associated to diabetes, which are not directly related to diabetes diagnosis criteria.