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Clustering-Based Recommendation System for Preliminary Disease Detection

Author

Listed:
  • Gourav Jain

    (Indian Institute of Technology, Roorkee, India)

  • Tripti Mahara

    (Christ University, India)

  • S. C. Sharma

    (Indian Institute of Technology, Roorkee, India)

  • Om Prakash Verma

    (Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India)

  • Tarun Sharma

    (Shobhit University, Gangoh, India)

Abstract

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 patient's 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%.

Suggested Citation

  • Gourav Jain & Tripti Mahara & S. C. Sharma & Om Prakash Verma & Tarun Sharma, 2022. "Clustering-Based Recommendation System for Preliminary Disease Detection," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 13(4), pages 1-14, August.
    • Handle: RePEc:igg:jehmc0:v:13:y:2022:i:4:p:1-14
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    References listed on IDEAS

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    1. Spatar, Daria & Kok, Orhun & Basoglu, Nuri & Daim, Tugrul, 2019. "Adoption factors of electronic health record systems," Technology in Society, Elsevier, vol. 58(C).
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