Research Article
COVID-19 Patient Care: A Content-Based Collaborative Filtering Using Intelligent Recommendation System
@INPROCEEDINGS{10.1007/978-3-030-76063-2_3, author={B. D. Deebak and Fadi Al-Turjman}, title={COVID-19 Patient Care: A Content-Based Collaborative Filtering Using Intelligent Recommendation System}, proceedings={Science and Technologies for Smart Cities. 6th EAI International Conference, SmartCity360°, Virtual Event, December 2-4, 2020, Proceedings}, proceedings_a={SMARTCITY}, year={2021}, month={5}, keywords={COVID-19 Healthcare Content-based Collaborative filtering Recommendation systems Accuracy}, doi={10.1007/978-3-030-76063-2_3} }- B. D. Deebak
Fadi Al-Turjman
Year: 2021
COVID-19 Patient Care: A Content-Based Collaborative Filtering Using Intelligent Recommendation System
SMARTCITY
Springer
DOI: 10.1007/978-3-030-76063-2_3
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.
