A data-driven model based on support vector machine to identify chronic hypertensive and diabetic patients

Abstract

Hypertension and diabetes are chronic conditions that have a considerable prevalence in the elderly. It is estimated that both hypertensive patients and people with diagnosed diabetes double cost of normotensive individuals and those in the absence of diabetes, respectively. It is therefore important to pay attention to these chronic conditions, both from a health and economical point of view, especially in scenarios with budget limitations. Clinical identification of chronic patients can be performed by feeding data of the patient encounters with the healthcare system to population classification systems such as Clinical Risk Groups (CRGs). CRGs classify individuals in unique and excluding health status categories taking both demographic and clinical information during certain period of time. In this work, we characterize healthy and chronic hypertensive and diabetic population at different chronic statuses (CRG) according to gender, age, diagnoses and drugs. After this characterization, we propose to use a supervised machine learning approach, in particular Support Vector Machines, to construct a data-driven model identifying the patient health status. We conclude that drugs and diagnoses are quite informative to discriminate patients with hypertension and diabetes, achieving promising results with the use of data-driven models.