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Predictive Analytics for Early Detection of COVID-19 by Fuzzy Logic

In: Decision Sciences for COVID-19

Author

Listed:
  • V. Kakulapati

    (Sreenidhi Institute of Science and Technology)

  • R. Sai Sandeep

    (Sreenidhi Institute of Science and Technology)

  • V. Kranthikumar

    (Sreenidhi Institute of Science and Technology)

Abstract

Today entire world is struggling, and significant cases are rising due to Coronavirus, namely COVID-19. Healthcare providers are busy in clinical trials to investigate the vaccine for this pandemic. If this virus attacks the person, nobody can know that person is going to be tested positive. This virus is spreading through the droplets of one person or dirty hands. The primary task of healthcare providers is to provide diagnostic product services at low costs and accurately diagnose patients. Machine learning methods can use for disease identification because they mainly apply to data and prioritize specific tasks’ outcomes. In this work, a multistage fuzzy rule-based algorithm for detection and CART algorithm is utilizing to produce the fuzzy rules. Implementation results exhibit that the proposed method differentiated the development of the disease prediction accuracy. The integration of these two techniques, multistage fuzzy rules and CART algorithms with unrelated data removal methods, could help predict disease. The proposed system can be helpful for healthcare providers in predicting the early stages of COVID-19.

Suggested Citation

  • V. Kakulapati & R. Sai Sandeep & V. Kranthikumar, 2022. "Predictive Analytics for Early Detection of COVID-19 by Fuzzy Logic," International Series in Operations Research & Management Science, in: Said Ali Hassan & Ali Wagdy Mohamed & Khalid Abdulaziz Alnowibet (ed.), Decision Sciences for COVID-19, chapter 0, pages 45-65, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-87019-5_3
    DOI: 10.1007/978-3-030-87019-5_3
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