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              <text>Clustering-Based Recommendation System for Preliminary Disease Detection</text>
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              <text>Collaborative Filtering; Coronavirus; COVID-19; Health Recommender Systems; Similarity Measure</text>
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              <text>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.</text>
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              <text>Jain G.; Mahara T.; Sharma S.C.; Verma O.P.; Sharma T.</text>
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              <text>International Journal of E-Health and Medical Communications, Vol-13, No. 4</text>
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              <text>2022-01-01</text>
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              <text>&lt;a href="https://doi.org/10.4018/IJEHMC.313191" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.4018/IJEHMC.313191&lt;/a&gt;
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              <text>All Open Access; Gold Open Access</text>
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              <text>ISSN: 1947315X</text>
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              <text>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</text>
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