Remote Health Patient Monitoring System for Early Detection of Heart Disease

Remote Health Patient Monitoring System for Early Detection of Heart Disease

Gokulnath Chandra Babu, Shantharajah S. P.
Copyright: © 2021 |Pages: 13
DOI: 10.4018/IJGHPC.2021040107
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Abstract

This paper presents a heart disease prediction model. Among the recent technology, internet of things-enabled healthcare plays a vital role. The medical sensors used in healthcare provide a huge volume of medical data in a continuous manner. The speed of data generation in IoT healthcare is high so the volume of data is also high. In order to overcome this problem, the proposed model is a novel three-step process to store and analyze the large volumes of data. The first step focuses on a collection of data from sensor devices. In Step 2, HBase has been used to store the large volume of medical sensor data from a wearable device to the cloud. Step 3 uses Mahout for devolving logistic regression-based prediction model. At last, ROC curve is used to find the parameters that cause heart disease.
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The Internet of Things is a inter connection of various devices for monitoring the physical activities and records continuously. The connected can share the data with each other with sensors and wireless networks (Baoyun, 2009). IoT based monitoring system’s uses a layered model to transfer or send the messages. Baumer et al proposed a scheme that says how Internet of Things can be used in the business environment (Doppler et al., 2009).

Fanucci et al (Manogaran & Lopez, 2017a) proposed a model that monitors the heart rate, respiratory rate, blood pressure, blood glucose level and it collects data and sends to the physician and in case of emergency it uses the records. Banos et al (Ishaq et al., 2013) designed a model that combined of mobile unit; medical sensors and a storage device, the medical sensors that used are blood pressure, body temperature, respiratory rate, blood glucose level. The mobile unit is used the data and gather the data and intimates if the medical values goes beyond the threshold values and with the extended value the emergency alert message is sent to the physician. Anliker et al (Kumar & Lee, 2011) designed a wearable device that can be worn in the human body and that monitors the heart rate, respiratory rate, blood pressure, glucose level, hemoglobin level. Suh et al (Manogaran et al., 2016) proposed model that detects the cardio vascular disease with the help of body mass index and the threshold values and if the value reaches more than the threshold value then the alert message is sent to the physician with the clinical records.

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