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Dergiler / 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
Sayfa
1–9
Yayın Tarihi
2023-06-30
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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

📌 3 Maddede Bu Makale

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Amaç & Hipotez:COVID-19 is a disease caused by the SARS-CoV-2 virus that emerged in December 2019 in Wuhan, China.
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Temel Bulgular & Veri:Intensive care units play an important role in saving the lives of severely ill COVID-19 patients.
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Sonuç & Katkı:Experimental results showed that SVM has 0.964 accuracy, 0.957 precision, 0.971 recall, and 0.963 F-score.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2019 Verileri
⚙️Değişkenler: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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Bu Makaleye Atıf Yap

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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Kaynakça (8)

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