Abstract
Biosensor analytics is a crucial tool for monitoring health conditions for patients and individuals with availability of wearable sensors and other devices. Biosensor studies can depict the detection of stress with precision. However, in this study, a review of feasible machine learning algorithms is reviewed with their comparative analysis as per healthcare data analytics. To design and implement a biosensor, suitable machine learning algorithm should be selected in order to detect anxiety and stress levels. Using machine learning algorithms, the analysis can be fault-tolerant and stress detection could be effective in terms of convenience.
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Dutta, A., Tripathy, H.K., Sen, A., Pani, L. (2021). Biosensor for Stress Detection Using Machine Learning. In: Mallick, P.K., Bhoi, A.K., Marques, G., Hugo C. de Albuquerque, V. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 1317. Springer, Singapore. https://doi.org/10.1007/978-981-16-1056-1_8
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