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Covid-19: Machine Learning Algorithms to Predict Mortality Rate for Advance Testing and Treatment

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1393)

Abstract

People worldwide are part of the fight against time to deal with the peculiarly invisible enemy—Covid-19 (Coronavirus). This viral pandemic spreads rapidly and is transmitted through direct contact with an infected person’s respiratory droplets. Multiple research teams around the globe are putting their efforts to gather essential data to develop solutions. Since machine learning in the health industry has made headlines, therefore we aim at contributing our bit through this paper by using two among various other machine learning algorithms—linear regression and multivariate regression, to evaluate medical data for predicting consequences that may be analyzed and cured effectively. The paper aims at ensuring clinical management by identifying the warning signs in an increasing number of cases and further predicts the total number of deaths. Linear regression and multivariate regression are to be the focused models so that the correlations between the features help in predicting the rise and fall of cases and quality of treatment. Machine learning algorithms help in producing reliable decisions based on the given data and it may be a boon to the medical sector to predict the number of death cases for analysis and treatment in advance.

Keywords

  • COVID-19
  • Machine learning
  • Linear regression
  • Multivariate regression

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Navadia, N.R., Kaur, G., Malik, I., Verma, L., Singh, T., Bhardwaj, H. (2021). Covid-19: Machine Learning Algorithms to Predict Mortality Rate for Advance Testing and Treatment. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_9

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