Real-time infectious disease endurance indicator system for scientific decisions using machine learning and rapid data processing Article Swipe
Shivendra Dubey
,
Dinesh Verma
,
Mahesh Kumar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.2062
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.2062
The SARS-CoV-2 virus, which induces an acute respiratory illness commonly referred to as COVID-19, had been designated as a pandemic by the World Health Organization due to its highly infectious nature and the associated public health risks it poses globally. Identifying the critical factors for predicting mortality is essential for improving patient therapy. Unlike other data types, such as computed tomography scans, x-radiation, and ultrasounds, basic blood test results are widely accessible and can aid in predicting mortality. The present research advocates the utilization of machine learning (ML) methodologies for predicting the likelihood of infectious disease like COVID-19 mortality by leveraging blood test data. Age, LDH (lactate dehydrogenase), lymphocytes, neutrophils, and hs-CRP (high-sensitivity C-reactive protein) are five extremely potent characteristics that, when combined, can accurately predict mortality in 96% of cases. By combining XGBoost feature importance with neural network classification, the optimal approach can predict mortality with exceptional accuracy from infectious disease, along with achieving a precision rate of 90% up to 16 days before the event. The studies suggested model’s excellent predictive performance and practicality were confirmed through testing with three instances that depended on the days to the outcome. By carefully analyzing and identifying patterns in these significant biomarkers insightful information has been obtained for simple application. This study offers potential remedies that could accelerate decision-making for targeted medical treatments within healthcare systems, utilizing a timely, accurate, and reliable method.
Related Topics
Concepts
Machine learning
Disease
Infectious disease (medical specialty)
Artificial intelligence
Pandemic
Computer science
Artificial neural network
Intensive care medicine
Medicine
Coronavirus disease 2019 (COVID-19)
Test data
Pathology
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7717/peerj-cs.2062
- OA Status
- gold
- Cited By
- 9
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401137671
All OpenAlex metadata
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https://openalex.org/W4401137671Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7717/peerj-cs.2062Digital Object Identifier
- Title
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Real-time infectious disease endurance indicator system for scientific decisions using machine learning and rapid data processingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-30Full publication date if available
- Authors
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Shivendra Dubey, Dinesh Verma, Mahesh KumarList of authors in order
- Landing page
-
https://doi.org/10.7717/peerj-cs.2062Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.7717/peerj-cs.2062Direct OA link when available
- Concepts
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Machine learning, Disease, Infectious disease (medical specialty), Artificial intelligence, Pandemic, Computer science, Artificial neural network, Intensive care medicine, Medicine, Coronavirus disease 2019 (COVID-19), Test data, Pathology, Programming languageTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 8, 2024: 1Per-year citation counts (last 5 years)
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