IoT: A Novel Method for White Coat Effect (WCE) Detection from Cloud for Improving Patient’s Treatments

Abstract

White coat effect (WCE) is a major issue in medical research due to the variation of multiple occasions of blood pressure (BP). BP is the most dynamic factor in a clinical problem which varies from time, place, season and movements of the body functions. BP reading is an important basic feature and initial step prediction of hypertension, diabetes and obesity. The World Health Organization (WHO) estimate of hypertension becomes the most significant premature death in the global world and 2025 hypertension increased nearly 1.56 billion people. The treatment of hypertension is mainly based on occasion level of BP. Medical diagnosis is a difficult outcome result of unstable measures from formal methods. Many researchers find the BP variations and provide improvement suggestions for treating a patient from clinical health data and home-based reading measurements. However, existing methods have no reliability, efficiency and accuracy in treatments. In this paper, a narrative technique is proposed as cloud computing reference model for justification of patient treatments which follows daily update in routine manner order to evade white coat syndrome (WCS). Additionally, the heartbeat rate, glucose monitor, pedometer measurement performance of patient health data from a cloud are examined and also compared the results of various scenarios. Moreover, our experimental analysis in Hadoop sandbox reveals 85% accuracy and safety clinical prediction in healthcare treatments. Thus, patient can prevent from an unnecessary excess of treatments and avoid tablets intake which leads to the cause of side effects or organ damage.