  This paper presents a verification-based methodology to validate the model of
personalized health conditions. The model identifies the values that may result
in unsafe, un-reachable, in-exhaustive, and overlapping states that otherwise
threaten patients' life by producing false alarms by accepting suspicious
behaviour of the target health condition. Contemporary approaches to validating
a model employ various testing, simulation and model checking techniques to
recognise such values and corresponding vulnerabilities. However, these
approaches are neither systematic nor exhaustive and thus fail to identify
those false values or vulnerabilities that estimate the health condition at
run-time based on the sensor or input data received from various IoT medical
devices. We have demonstrated the validation methodology by validating our
example multi-level model that describes three different scenarios of Diabetes
health conditions.
