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An Analysis of Supervised Machine Learning Algorithms for COVID-19 Diagnosis

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Proceedings of Seventh International Congress on Information and Communication Technology

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 465))

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Abstract

The global pandemic COVID-19 is infectious disease which produced devasting effect to mankind and entire health community. The disease shown the multiple variants by the time and due to the severity, many people have lost their lives. The predetermination and early prediction based on symptoms can increase the survival rate. Many researchers have proposed the prediction model using conventional machine learning techniques and ensemble model. With this research work, the comparative study of machine learning techniques is shown. The study shows the utilisation and working process of supervised learning in terms of classification of categorical data. The proposed comparative approach predicts the COVID results based on specific features. The algorithm used is random forest, decision tree, K-nearest neighbour, and Naïve Bayes classifier. The model is evaluated based on the performance metrics such as accuracy, precision, recall, and F1 score. With fitting the model on open-source data, it is found the performance is improved, and the proposed model selection is the best choice for time constraint application.

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References

  1. Akalanka BA, Senevirathne KDA, Dias MH et al (2021) Smart assistant to ease the process of COVID-19 and pneumonia detection, 0270–0275. https://doi.org/10.1109/iemcon53756.2021.9623105

  2. Aljameel SS, Khan IU, Aslam N et al (2021) Machine learning-based model to predict the disease severity and outcome in COVID-19 patients. Sci Program. https://doi.org/10.1155/2021/5587188

  3. Avr S Samples to COVID-19 in Israel Dataset. https://www.kaggle.com/shiravr/israel-covid19-dataset

  4. Bankar A, Padamwar K, Jahagirdar A (2020) Symptom analysis using a machine learning approach for early stage lung cancer. In: 2020 3rd international conference on intelligent sustainable systems (ICISS), 246–250. https://doi.org/10.1109/ICISS49785.2020.9315904

  5. Devi RG, Sumanjani P (2015) Improved classification techniques by combining KNN and random forest with Naive Bayesian classifier. In: 2015 IEEE international conference on engineering and technology (ICETECH), 20–23. https://doi.org/10.1109/ICETECH.2015.7274997

  6. Devika R, Avilala SV, Subramaniyaswamy V (2019) Comparative study of classifier for chronic kidney disease prediction using naive bayes, KNN and random forest. In: 2019 3rd international conference on computing methodologies and communication (ICCMC), 679–684. https://doi.org/10.1109/ICCMC.2019.8819654

  7. Dharani N, Kirupa Krishnan G (2021) ANN based COVID-19 prediction and symptoms relevance survey and analysis. In: 2021 5th international conference on computing methodologies and communication (ICCMC), 1805–1808. https://doi.org/10.1109/ICCMC51019.2021.9418448

  8. Dutta S, Bandyopadhyay SK (2021) Diabetes prediction using machine learning approaches. Lect Notes Data Eng Commun Technol 64:179–202. https://doi.org/10.1007/978-981-16-0538-3_9

    Article  Google Scholar 

  9. KangYoon S (2021) The prediction model for classification of COVID-19 infected patients using vital sign, 678–681

    Google Scholar 

  10. Khan Z, Mishra DK, Sharma V, Sharma A (2020) Empirical study of various classification techniques for heart disease prediction. In: 2020 IEEE 5th international conference on computing communication and automation (ICCCA), 57–62. https://doi.org/10.1109/ICCCA49541.2020.9250852

  11. Koushik C, Bhattacharjee R, Hemalatha CS (2021) Symptoms based early clinical diagnosis of COVID-19 cases using hybrid and ensemble machine learning techniques. In: 2021 5th international conference on computer, communication and signal processing (ICCCSP), 59–64. https://doi.org/10.1109/ICCCSP52374.2021.9465494

  12. Lakshmanarao A, Srisaila A, Kiran TSR (2021) Heart disease prediction using feature selection and ensemble learning techniques. In: 2021 3rd international conference on intelligent communication technologies and virtual mobile networks (ICICV), 994–998. https://doi.org/10.1109/ICICV50876.2021.9388482

  13. Liu Y, Zhang M, Fan Z, Chen Y (2020) Heart disease prediction based on random forest and LSTM. In: 2020 2nd international conference on information technology and computer application (ITCA) 630–635. https://doi.org/10.1109/ITCA52113.2020.00137

  14. Nandy S, Adhikari M (2021) Intelligent health monitoring system for detection of symptomatic/asymptomatic COVID-19 patient. IEEE Sens J 21:20504–20511. https://doi.org/10.1109/JSEN.2021.3096425

    Article  Google Scholar 

  15. Nayeem MJ, Rana S, Alam F, Rahman MA (2021) Prediction of hepatitis disease using K-nearest neighbors, Naive Bayes, support vector machine, multi-layer perceptron and random forest. In: 2021 international conference on information and communication technology for sustainable development (ICICT4SD)—proceedings, 280–284. https://doi.org/10.1109/ICICT4SD50815.2021.9397013

  16. Pal OK (2021) Skin disease classification : a comparative analysis of K-Nearest Neighbors (KNN) and random forest algorithm. 14–16

    Google Scholar 

  17. Rohini M, Naveena KR, Jothipriya G, et al (2021) A Comparative Approach to Predict Corona Virus Using Machine Learning. In: Proceedings International Conference on Artificial Intelligence and Smart Systems ICAIS 2021, 331–337. https://doi.org/10.1109/ICAIS50930.2021.9395827

  18. Sasikala BS, Biju VG, Prashanth CM (2017) Kappa and accuracy evaluations of machine learning classifiers. In: 2017 2nd IEEE international conference on recent trends in electronics, information and communication technology (RTEICT), 20–23. https://doi.org/10.1109/RTEICT.2017.8256551

  19. Shrivastava P, Singh A, Agarwal S et al (2021) Covid detection in CT and X-Ray images using ensemble learning. In: 2021 5th international conference on computing methodologies and communication (ICCMC), 1085–1090. https://doi.org/10.1109/ICCMC51019.2021.9418308

  20. Da Silva RA, Canuto AMDP, Barreto CADS, Xavier JC (2021) Automatic recommendation method for classifier ensemble structure using meta-learning. IEEE Access 9:106254–106268. https://doi.org/10.1109/ACCESS.2021.3099689

    Article  Google Scholar 

  21. Singla J, Singh A, Kaur H (2020) Review on artificial intelligence techniques for medical diagnosis. In: 2020 3rd international conference on intelligent sustainable systems (ICISS), 735–738. https://doi.org/10.1109/ICISS49785.2020.9316035

  22. Ye H, Wu P, Zhu T et al (2021) Diagnosing coronavirus disease 2019 (COVID-19): efficient harris hawks-inspired fuzzy K-nearest neighbor prediction methods. IEEE Access 9:17787–17802. https://doi.org/10.1109/ACCESS.2021.3052835

    Article  Google Scholar 

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Authors and Affiliations

  1. Namibia University of Science and Technology, Windhoek, Namibia

    Arpit Jain & Dharm Singh Jat

Authors
  1. Arpit Jain
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  2. Dharm Singh Jat
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Correspondence to Arpit Jain .

Editor information

Editors and Affiliations

  1. Middlesex University, London, UK

    Xin-She Yang

  2. The University of Reading, Reading, UK

    Simon Sherratt

  3. JIS University, Kolkata, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Jain, A., Jat, D.S. (2023). An Analysis of Supervised Machine Learning Algorithms for COVID-19 Diagnosis. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 465. Springer, Singapore. https://doi.org/10.1007/978-981-19-2397-5_71

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  • DOI: https://doi.org/10.1007/978-981-19-2397-5_71

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-2396-8

  • Online ISBN: 978-981-19-2397-5

  • eBook Packages: EngineeringEngineering (R0)

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