Early Strokes Detection of Patient and Health Monitoring System Based On Data Analytics Using K-Means Algorithm

Abstract

. The processing chain of scientific information in particular consists of information series, information storage, data sharing, and records evaluation. In the prevailing device, there is lots of improvisation wished for our health care device. The existing technique our affected person monitoring is a guide and time-consuming process. To conquer the task the proposed work proposed actual data series from sensors, IoT-primarily based totally sharing, and information analytics. This proposed device gives the gain for the respective medical doctor to display the affected person's health 24*7 regardless of geographical location. Example: The medical doctor can display the affected person's health even after the affected person receives discharged. This proposed work implies a health sensor named heartbeat sensor to display affected person fitness. The affected person information is monitoring through the sensorsand transmitted to the Arduino. The actual-time statistics from the COM port have acquired the usage of Net beans and stored in an SQL database. The actual-time information may be monitored with the aid of using each affected person and medical doctor. The real-time statistics are processed from Net beans as datasheet to R programming for statistical evaluation. For Clustering, we use the K-Means algorithm and for Classification, we use the Support Vector Machine. Also relying on the affected person's health situations emergency pills or injections are counseled routinely with the aid of using our device. Also, the affected person statistics have encrypted the usage of an ABE (Attribute-based Encryption) set of rules and saved in the public cloud particularly Dropbox. Thus invoking the statistics evaluation approach facilitates in identifying early stroke in the sufferers and offer medicinal diagnosis immediately.