Improving disease prediction using ICD-9 ontological features

Disease prediction has become important in a variety of applications such as health insurance, tailored health communication and public health. Disease prediction is usually performed using publically available datasets such as HCUP, NHANES or MDS that were initially designed for health reporting or health cost evaluation but not for disease prediction. In these datasets, medical diagnoses are traditionally arranged in "diagnose-related groups" (DRGs). In this paper we compare the disease prediction based on crisp DRG features with the results obtained employing a new set of features that consist of the fuzzy membership of patient diagnoses in the DRG groups. The fuzzy membership features were computed using an ICD-9 ontological similarity approach. The prediction results obtained on a subset of 9,000 patients from the 2005 HCUP data representing three diseases (diabetes, atherosclerosis and hypertension) using two classifiers (random forest and SVM trained on 21,000 samples) show significant (about 10%) improvement as measured by the area under the ROC curve (AROC).