Abstract
In several disciplines, especially in personalized healthcare, the Internet of Things (IoT) is gaining increasing interest from a technological point of view. However, in both cases, effective diagnosis of heart disease and consultation of a patient for 24 h by a specialist are not accessible due to many factors. In this research, we present a real-time monitoring system based on concepts of IoT and machine learning for the detection of heart diseases through a portable setup accessible even in remote areas. The system integrates various sensor nodes for heartbeat, blood pressure, temperature, and RFID reading onto a Raspberry Pi to maintain and build on a database for optimized results. In order to evaluate the efficiency of heart disease diagnosis, the proposed algorithms are tested using commonly available open-access databases. Including parameter optimization, a comparative study of statistical machine learning models has been applied with a random forest algorithm (0.868) producing best performance followed by SVM (0.852), k-NN (0.836), and decision tree (0.786). The database building and prediction are based on the cloud which further improves on the computational and time efficiency of the system.
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Salvi, S., Dhar, R., Karamchandani, S. (2021). IoT-Based Framework for Real-Time Heart Disease Prediction Using Machine Learning Techniques. In: Singh, J., Kumar, S., Choudhury, U. (eds) Innovations in Cyber Physical Systems. Lecture Notes in Electrical Engineering, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-16-4149-7_43
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DOI: https://doi.org/10.1007/978-981-16-4149-7_43
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