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Early diagnosis is important for type 2 diabetes (T2D) to improve patientprognosis, prevent complications and reduce long-term treatment costs. Wepresent a novel risk profiling approach based exclusively on health expendituredata that is available to Belgian mutual health insurers. We used expendituredata related to drug purchases and medical provisions to construct models thatpredict whether a patient will start glucose-lowering pharmacotherapy in thecoming years, based on that patient's recent medical expenditure history. Thedesign and implementation of the modeling strategy are discussed in detail andseveral learning methods are benchmarked for our application. Our bestperforming model obtains between 74.9% and 76.8% area under the ROC curve,which is comparable to state-of-the-art risk prediction approaches for T2Dbased on questionnaires. In contrast to other methods, our approach can beimplemented on a population-wide scale at virtually no extra operational cost.Possibly, our approach can be further improved by additional information aboutsome risk factors of T2D that is unavailable in health expenditure data.

Building Classifiers to Predict the Start of Glucose-Lowering Pharmacotherapy Using Belgian Health Expenditure Data