Abstract
In a hospital, the organized provision of medical care to a community or individuals, it is obligatory to always monitor the patients physiological parameters. At a time doctors cannot monitor more than one patient, but Wireless Body Sensor Network (WBSN) can be used to build a patient monitoring system which offers the ability to move and pliability to monitor patients health condition. Some systems prediction may not be accurate all the time and hence lead to False Positive Rate (FPR). In this paper, we have calculated specificity, sensitivity, and accuracy of different classifiers (K-NN, QD) for 11 patient of Physiological data. In machine learning, validation of the model is referred to as the procedure where a trained model is evaluated with a testing data.
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Vaidya, M., Dahatkar, N., Kolhe, B. (2019). Predictive Monitoring System Using K-NN, QDC Classifiers of Physiological Data. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 107. Springer, Singapore. https://doi.org/10.1007/978-981-13-1747-7_18
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DOI: https://doi.org/10.1007/978-981-13-1747-7_18
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