Machine Learning-Based Asthma Risk Prediction Using IoT and Smartphone Applications

In this paper, we present an asthma risk prediction tool based on machine learning (ML). The entire tool is implemented on a smartphone as a mobile-health (m-health) application using the resources of Internet-of-Things (IoT). Peak Expiratory Flow Rates (PEFR) are commonly measured using external in...

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Veröffentlicht in:	IEEE access 2021, Vol.9, p.118708-118715
Hauptverfasser:	Bhat, Gautam S., Shankar, Nikhil, Kim, Dohyeong, Song, Dae Jin, Seo, Sungchul, Panahi, Issa M. S., Tamil, Lakshman
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Asthma Asthma prediction Atmospheric modeling convolutional neural network Convolutional neural networks Diseases Error analysis Flow velocity Flowmeters Internet of Things Internet-of-Things (IoT) Machine learning Meteorological data Meteorology Neural networks Particulate emissions particulate matter (PM) peak expiratory flow rates (PEFR) Predictive models Raspberry Pi Real-time systems Respiratory system Risk Smartphones
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