Clustering-Based Recommendation System for Preliminary Disease Detection

Title

Clustering-Based Recommendation System for Preliminary Disease Detection

Creator

Jain G.; Mahara T.; Sharma S.C.; Verma O.P.; Sharma T.

Description

The catastrophic outbreak COVID-19 has brought threat to the society and also placed severe stress on the healthcare systems worldwide. Different segments of society are contributing to their best effort to curb the spread of COVID-19. As a part of this contribution, in this research, a clustering-based recommender system is proposed for early detection of COVID-19 based on the symptoms of an individual. For this, the suspected patients symptoms are compared with the patient who has already contracted COVID-19 by computing similarity between symptoms. Based on this, the suspected person is classified into either of the three risk categories: high, medium, and low. This is not a confirmed test but only a mechanism to alert the suspected patient. The accuracy of the algorithm is more than 85%. 2022 IGI Global. All rights reserved.

Source

International Journal of E-Health and Medical Communications, Vol-13, No. 4

Date

2022-01-01

Publisher

IGI Global

Subject

Collaborative Filtering; Coronavirus; COVID-19; Health Recommender Systems; Similarity Measure

Coverage

Jain G., Indian Institute of Technology, Roorkee, India; Mahara T., Christ University, India; Sharma S.C., Indian Institute of Technology, Roorkee, India; Verma O.P., Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India; Sharma T., Shobhit University, Gangoh, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 1947315X

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.4018/IJEHMC.313191

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202576722&doi=10.4018%2fIJEHMC.313191&partnerID=40&md5=a9653cf3a03b082fad618db4417b30ba