  Leveraging health administrative data (HAD) datasets for predicting the risk
of chronic diseases including diabetes has gained a lot of attention in the
machine learning community recently. In this paper, we use the largest health
records datasets of patients in Ontario,Canada. Provided by the Institute of
Clinical Evaluative Sciences (ICES), this database is age, gender and
ethnicity-diverse. The datasets include demographics, lab measurements,drug
benefits, healthcare system interactions, ambulatory and hospitalizations
records. We perform one of the first large-scale machine learning studies with
this data to study the task of predicting diabetes in a range of 1-10 years
ahead, which requires no additional screening of individuals.In the best setup,
we reach a test AUC of 80.3 with a single-model trained on an observation
window of 5 years with a one-year buffer using all datasets. A subset of top 15
features alone (out of a total of 963) could provide a test AUC of 79.1. In
this paper, we provide extensive machine learning model performance and feature
contribution analysis, which enables us to narrow down to the most important
features useful for diabetes forecasting. Examples include chronic conditions
such as asthma and hypertension, lab results, diagnostic codes in insurance
claims, age and geographical information.
