Abstract
COVID-19 is a more transferable illness caused by a new novel coronavirus. It is highly emerging with efficient biosensors such as sensitive and selective that afford the diagnostic tools to infer the disease early. It can maintain a personalized healthcare system to evaluate the growth of disease under proper patient care. To discover as a personalized technology, the healthcare system prefers collaborative filtering. It can effectively deal with cold-start and sparse-data to conduct useful extensions. Due to the continuous expansion of scaling data in a medical scenario, content-based, collaborative filtering, and similarity metrics are preferred. It relies on the most similar social users or threats when the information is large. Many neighbors gain importance to obtain a set of users with whom a target user is likely to match. Forming communities reveal vulnerable users and also reduce the challenges of collaborative filtering like data-sparsity and cold-start problems. Thus, this framework proposes content-based collaborative filtering using intelligent recommendation systems (CCF-IRS) based on high correlation and shortest neighbor in the social community. The result is shown that the proposed CCF-IRS achieves better accuracy than the existing algorithms.
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Deebak, B.D., Al-Turjman, F. (2021). COVID-19 Patient Care: A Content-Based Collaborative Filtering Using Intelligent Recommendation System. In: Paiva, S., Lopes, S.I., Zitouni, R., Gupta, N., Lopes, S.F., Yonezawa, T. (eds) Science and Technologies for Smart Cities. SmartCity360° 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 372. Springer, Cham. https://doi.org/10.1007/978-3-030-76063-2_3
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