Policy-Based Diabetes Detection using Formal Runtime Verification Monitors

Diabetes is a global health threat, and its prevalence is rising at an alarming rate. Diabetes is the cause of severe complications in vital organs of the body. So, diabetes must be detected early for timely treatment and to prevent the condition from escalating to severe consequences. Many AI and machine learning approaches have been proposed for the non-invasive continuous monitoring of diabetes. However, using such informal methods in healthcare monitoring raises concerns about reliability. Furthermore, deploying an AI-based solution to continuously monitor a person's health state on resource-constrained embedded devices is a concern. We overcome these shortcomings in this work by proposing a formal runtime monitoring system for the first time for diabetes detection using Electrocardiogram (ECG) sensing. We implement a data mining model from the ECG features to infer ECG policies and thereby synthesize a formal verification monitor based on the policies. Using a diabetes dataset, we evaluate the verification monitor's performance compared to other proposed models.