An examination of disease prediction using Transformer from medical diagnostic data

Abstract

Lifestyle Diseases are caused and progress due to lifestyle behaviors encompassing dietary patterns, physical activity, and cigarette usage. Lifestyle diseases are characterized by their insidious nature, often lack discernible subjective symptoms, and increase the likelihood of severe illnesses going unnoticed. Consequently, preventive health care, entailing health check-ups and comprehensive medical examinations, garners escalating consideration for the timely detection, treatment, and prevention of severe illnesses. In this paper, we propose a Transformer-based method for predicting diabetes, which is a typical lifestyle disease, to predicate on medical data obtained from various health check-ups and other sources. Furthermore, we evaluated our method that appropriately accounts for the missing values inherent to medical data, a distinction stemming from differences in the number of examination items and the frequency of medical examinations. The Transformer-based method shows 83.2% of the AUC score.