Life Style Related Risk Association Mining

Abstract

IoT application in health care provides ways to monitor and collect health related biomarkers, in particular, life-style related data, by recording and analyzing long-term data, to provide insight to patients’ status. In order to make most use of this application, linking the collected patients’ data with a disease predictive model will generate a personalized disease progression and predictions. It is also important to understand one’s health risks in order to benefit from new research about specific diseases and plan for preventive monitoring. Risk factors for a disease are results of various medical researches. In this paper, we propose an approach for risk factor selection and mining.