Main Article Content
Abstract
There are enormous amount of IoT devices were developed in the medical field. IoT devices plays a requisite role in monitoring the health conditions of a patient. For medical practitioners, it is very important to monitor the physiological conditions of patient. The early prediction of unusual heart conditions is very essential to discover heart problems. This paper proposes a wearable sensor device for earlier prediction of heart diseases. This scheme involves measurement and evaluation of likelihood of heart disease in a patient. We use NAÏVE BAYESIAN CLASSIFICATION technique to detect the heart diseases before occurring. Naïve Bayesian classifier is a easy-to-do and influential algorithm for the classification task, based on bayes’ theorem. The proposing IoT related health tracking scheme consists of three phases, namely phase 1 data acquisition, phase 2 data storage, and phase 3 data analytics. Once, the patient’s physiological data is collected through wearable sensor device, the data will be transferred to Amazon S3 (storage device) using s3cmd method, then the stored information will be transferred to Apache HBase (Hadoop distributed file system) by using Apache Pig(platform for analysing data). The collected data will be classified based on NAÏVE BAYESIAN CLASSIFICATION technique. Apache Mahout is used for implementing NAÏVE BAYESIAN prediction method. After applying this technique, if the likelihood of getting heart diseases is higher,then the information will be immediately transferred to the doctor and the doctor will provide emergency services to the patient. So that, we can prevent the patient from heart diseases.