Abstract
The epidemic taught us to keep digital health records. It also showed us how wearable observing gear, video conferences, and AI-powered chatbots may give good treatment remotely. Real-time data from health care devices across the globe helped attack and track the infection. Biomedical imaging, sensors, and machine learning have improved health in recent years. Medical care and biomedical sciences are now information science sectors that demand enhanced data mining approaches. Biomedical data have high dimensionality, class irregularity, and few tests. AI uses information and computations to imitate how people learn, continually improving its accuracy. ML is an important part of information science. Calculations utilising measurable methods reveal essential information about information mining operations. This paper explains and compares ML algorithms that can detect diseases sooner. We summarise ML’s algorithms and processes to extract information for a data-driven society.
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Alduailij, M., Mishra, A., Zamzami, I.F., Psannis, K. (2023). An Analysis of Machine Learning Algorithms for Smart Healthcare Systems. In: Nedjah, N., Martínez Pérez, G., Gupta, B.B. (eds) International Conference on Cyber Security, Privacy and Networking (ICSPN 2022). ICSPN 2021. Lecture Notes in Networks and Systems, vol 599. Springer, Cham. https://doi.org/10.1007/978-3-031-22018-0_8
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