Abstract
Researchers have been endlessly contributing to discover cardiovascular diseases (CVDs) in an early stage using various intelligent practices when a report published by the WHO―World Health Organization—shows internationally CVDs are the primary reason for human deaths in 2017. They have been trying to control and prevent through various Global Hearts Initiatives. WHO also mentioned major risk factors for cardiovascular disease. These factors might be the use of tobacco, physical activity, consumption of high salt in daily food, consumption of foods with TRANS-FATTY acids, and high blood pressure ( (2017). Cardiovascular diseases (CVDs). Available: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)). As per number published by the WHO, around 17.9 million (179 lakh) deaths were responsible due to cardiovascular in 2016. Heart attacks and strokes were the topmost reasons. Internet of Things (IoT) empowers people to get an advanced level of automating by evolving a system with the help of sensors, interconnected devices, and the Internet. In the healthcare segment, patient monitoring is a very critical and most vital activity since a small delay in decision related to patients’ treatment may cause permanent disability or maybe death. Most ICU devices are furnished with various sensors to measure health parameters but to monitor it all the time is still a challenging job. In our research, we have proposed an IOT based intelligent model, which captures the various body parameters using the bedside monitor and discover the early stage CVDs disease using supervised machine learning classification algorithms. To improve the accuracy of the model, we have also used multiple algorithms to achieve better accuracy.
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Acknowledgements
We would like to thank Dr. Amol Agarwal, Dr. Pritesh Shah, and Dr. Mukul Shah for giving their worthwhile guidance to understand different medical parameters. We would also like to appreciate Mr. Aman Ajmera for storing a Framingham heart study dataset to examine the pattern [2]. Last but not least those patients who have actively engaged in research and willing to share their medical healthcare report with us. We acknowledged the hospitals that give us consent to access their medical devices to test our model. We are highly thankful to all the below-mentioned researchers in reference, who has encouraged us by their study and publications.
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Prajapati, B., Parikh, S., Patel, J. (2022). An Implementation of IRTBS—for Observing Cardiac Patients. In: Senjyu, T., Mahalle, P.N., Perumal, T., Joshi, A. (eds) ICT with Intelligent Applications. Smart Innovation, Systems and Technologies, vol 248. Springer, Singapore. https://doi.org/10.1007/978-981-16-4177-0_21
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DOI: https://doi.org/10.1007/978-981-16-4177-0_21
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