Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India

Research Article

An Efficient way of Predicting Covid-19 using Machine and Deep Learning Algorithms

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  • @INPROCEEDINGS{10.4108/eai.7-6-2021.2308785,
        author={Meenakshi  N and Kumaresan  Angappan and Sandhya  A and Naga Susmitha V and Vaishnavi  K},
        title={An Efficient way of Predicting Covid-19 using Machine and Deep Learning Algorithms},
        proceedings={Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India},
        publisher={EAI},
        proceedings_a={I3CAC},
        year={2021},
        month={6},
        keywords={covid-19 clinical data analysis machine  learning deep learning blood test ct scan efficient random forest modified densenet121},
        doi={10.4108/eai.7-6-2021.2308785}
    }
    
  • Meenakshi N
    Kumaresan Angappan
    Sandhya A
    Naga Susmitha V
    Vaishnavi K
    Year: 2021
    An Efficient way of Predicting Covid-19 using Machine and Deep Learning Algorithms
    I3CAC
    EAI
    DOI: 10.4108/eai.7-6-2021.2308785
Meenakshi N1,*, Kumaresan Angappan1, Sandhya A1, Naga Susmitha V1, Vaishnavi K1
  • 1: Department of Information Technology, Hindustan Institute of Technology and Science, Chennai, India
*Contact email: abu221985@gmail.com

Abstract

Recently the novel coronavirus disease pushed the world into the dramatic situation. The tough thing to deal with novel corona virus is the prediction. In the beginning RT PCR test is the golden standard test for the prediction of COVID, which takes more time, more licensed laboratories, trained personnel and prediction accuracy will be not fruitful. In our System, We used current technology for the prediction, which involves: An Efficient Random Forest, a machine learning classification model which predicts whether the person is Corona affected or not using routine blood reports and a deep learning model, Modified DenseNet121 which was pre-trained to predict theCovid using CT scan images. To analyze the machine learning model performance, 5744 blood report samples have been collected from Kagglerepository;similarly, 2482 CT scan samples have been collected from the Kaggle repository, for prediction using Random Forest and DenseNet121 model. The proposed model which is developed using machine and deep learning techniques can be deployed easily and can be used for rapid and accurate prediction of Covid19.