Machine Learning-Based Identification of Obesity from Positive and Unlabelled Electronic Health Records.

INTRODUCTION Prevalence of overweight and obesity are increas- ing in the last decades, and with them, diseases and health conditions such as diabetes, hypertension or cardiovascular diseases. However, hos- pital databases usually do not record such conditions in adults, neither anthropomorfic measures that facilitate their identification. METHODS We implemented a machine learning method based on PU (Positive and Unlabelled) Learning to identify obese patients without a diagnose code of obesity in the health records. RESULTS The algorithm presented a high sensitivity (98%) and predicted that around 18% of the patients without a diagnosis were obese. This result is consistent with the report of the WHO.