The State-of-the-art Machine Learning In Prediction Covid-19 Fatality Cases

The State-of-the-art Machine Learning In Prediction Covid-19 Fatality Cases

Maleerat Maliyaem

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Nguyen Minh Tuan

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α King Mongkut's University of Technology North Bangkok

The State-of-the-art Machine Learning In Prediction Covid-19 Fatality Cases

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Abstract

Day by day, the number of confirmed Covid-19 cases significantly increases all over the world. In India, the second wave of coronavirus has come back and created a disastrous impact. On April 3rd, India continuously recorded the highest number of daily cases globally, according to Financial Times, there was a scarcity of crematoriums and burial grounds due to the high number of corpses. The outbreak of death cases was an unprecedented circumstance, hence, there was a shortage of medical necessities. Prediction of death cases could help the government to manage the medical facilities such as beds and oxygen supply for the hospital. Machine learning could be used to analyze and predict fatality cases. PySpark library is used to process raw data and update new data each day, as the library allows the processing of a large amount of raw data efficiently. By using the Naïve Bayes algorithm available in PySpark, the prediction accuracy has increased to 81.3%.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Maleerat Maliyaem. 2026. \u201cThe State-of-the-art Machine Learning In Prediction Covid-19 Fatality Cases\u201d. Global Journal of Computer Science and Technology - B: Cloud & Distributed GJCST-B Volume 22 (GJCST Volume 22 Issue B1): .

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GJCST Volume 22 Issue B1
Pg. 57- 63

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-B Classification: DDC Code: 006.312 LCC Code: QA76.9.D343

Submission ReceivedAugust 13, 2022
Peer Review Double Blind
Handling Editor
Accepted August 20, 2022
Published July 21, 2022

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November 21, 2022

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