Abstract
Machine learning can be used across several spheres around the planet. The medical industry is not different. Health monitoring using wearable sensor enables us to go with Internet of Medical Things (IoMT). It enables the users to obtain the real time data i.e. live monitoring for manual prediction of user’s health, using machine learning techniques. Data Generation is one of the most challenging problems which have been faced by many researchers. As the volume of obtained data is very large machine learning techniques need to be used. Machine Learning can predict the presence/absence of locomotor disorders and Heart diseases in our body. Such information, if predicted well ahead of time can provides essential insights to physicians who could subsequently schedule their treatment and diagnosis for their patients. In this paper, various machine learning algorithms have been implemented to predict the heart disease. 88.59% accuracy was obtained by using logistic regression with majority voting which is better than the existing techniques.
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Divya, K., Sirohi, A., Pande, S., Malik, R. (2021). An IoMT Assisted Heart Disease Diagnostic System Using Machine Learning Techniques. In: Hassanien, A.E., Khamparia, A., Gupta, D., Shankar, K., Slowik, A. (eds) Cognitive Internet of Medical Things for Smart Healthcare. Studies in Systems, Decision and Control, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-030-55833-8_9
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