A Novel IoT-Enabled Device for Early Prediction and Detection of Chronic Disease

Abstract

Chronic diseases is increasing day-by-day according to world health organization (WHO), and now its health challenge worldwide that requires early prediction and detection of accurate diagnosis of a chronic diseases. In this paper is to design a device to get the reading from medical sensor calculate the early warning score (EWS) by using machine learning algorithms. The main target of this paper is to offer actual readings of medical sensor which gives blood pressure (BP) and pulse rate (PR) readings as per the prescribe time interval suggest by the medical professionals. EWS calculated by using machine learning (ML) algorithms, this paper includes K-NN, Naïve Bayes, support vector machine, and random forest for early prediction of chronic diseases. In this Naïve Bayes, gives 99.44% accuracy result which is a crucial aspect of in case of ICU admission due to chronic diseases. The paper target to decrease healthcare expenses and identify wrongdoings in the chronic diseases.