Predicting Chronic Disease by Monitoring Patients Updating Sensor Information with Big Health Application System
DOI:
https://doi.org//10.32628/CSEIT1953169Keywords:
Big Data, Internet of things(IoT), Cloud Computing, Big Health, Naïve Bayes, KNN, Decision Tree, CNN-UDRP, CNN-MDRPAbstract
Nowadays the advances in the computer technology have validated great development on healthcare technologies in numerous fields. However these new technologies have made also healthcare data not only much bigger but also much more difficult to handle and process. Currently the peoples are leading to death because of the proper distribution of medical resources over the world. Cloud and big data not only are important techniques but also are gradually becoming the trends in healthcare innovation. However these problems can be greatly solved by building a healthcare system with the help of these new technologies. But the greatest challenge of building a comprehensive healthcare system is in the handling of the heterogeneous healthcare data captured from multiple sources. In-order to provide a more convenient service and environment of healthcare, this paper proposes a big health application system based on the health internet of things and big data. The world is confronting issues, for example, uneven conveyance of restorative assets, the developing endless maladies, and the expanding restorative costs. Mixing the most recent data innovation into the human services framework will enormously alleviate the issues. So building huge well being application framework by adequately coordinating medicinal well being assets utilizing keen terminals, well being Internet of Things (IoT), enormous information and distributed computing is the significant method to unravel the above issues. Also in this work proposes a new convolutional neural network based multi-modal disease risk prediction (CNN-MDRP) algorithm using structured and unstructured data from hospital.
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