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Journals / NATURENGS / 2023 / Cilt: 4 Sayı: 1

Machine Learning Based A Comparative Analysis for Predicting Intensive Care Unit Admission of COVID-19 Cases

Anıl UTKU
Ümit CAN
Journal
NATURENGS
Pages
1–9
Publication Date
2023-06-30
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results showed that SVM has 0.964 accuracy, 0.957 precision, 0.971 recall, and 0.963 F-score.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:COVID-19 is a disease caused by the SARS-CoV-2 virus that emerged in December 2019 in Wuhan, China.
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Key Findings & Data:Intensive care units play an important role in saving the lives of severely ill COVID-19 patients.
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Conclusion & Impact:Experimental results showed that SVM has 0.964 accuracy, 0.957 precision, 0.971 recall, and 0.963 F-score.
🔬Design:Modeling & Simulation
📍Scope:2019 Verileri
⚙️Variables:COVID-19 · Machine Learning · Intensive Care Unit

Abstract

COVID-19 is a disease caused by the SARS-CoV-2 virus that emerged in December 2019 in Wuhan, China. This virus, which can be transmitted quickly, spread worldwide quickly, causing many people to be infected and even killed. The rapid course of the epidemic made managing medical resources difficult. Intensive care units play an important role in saving the lives of severely ill COVID-19 patients. In this study, a machine learning-based detection system was developed to predict the hospitalization of COVID-19 patients in intensive care units. Using a dataset of demographic characteristics and clinical findings of COVID-19 patients, DT, kNN, LR, MLP, NB, RF, and SVM were compared in practice using accuracy, recall, precision, and F-score. Experimental results showed that SVM has 0.964 accuracy, 0.957 precision, 0.971 recall, and 0.963 F-score.

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Cite This Article

UTKU, A., CAN, Ü. (2023). Machine Learning Based A Comparative Analysis for Predicting Intensive Care Unit Admission of COVID-19 Cases. NATURENGS, Cilt: 4 Sayı: 1, 1–9. https://ajindex.com/articles/946498

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