COVID-19 Şiddeti Ve Mortalitesinin Kan Parametrelerinden Kolektif Öğrenme Yöntemleri İle Tespiti

Year 2023, Volume: 13 Issue: 2, 316 - 328, 29.12.2023

Gizemnur Erol , Betül Uzbaş

Abstract

COVID-19, yüksek yayılım hızına ve Akut Solunum Sıkıntısı Sendromuna (ARDS) neden bir pandemidir. Enfekte bireylerde gelişen şiddetli pnömoni, çok fazla hastanın Yoğun Bakım Ünitesine (ICU) kabul edilmesine neden olmuştur. Bu da, sağlık sistemlerinde kapasitelerin aşılarak benzeri görülmemiş bir baskı meydana getirmiştir. Sağlık sistemlerinin aktif kalabilmesi ve ICU’ya yatması gereken hastaların durumlarının kritikleşmemesi için bu hastalığın prognozunun belirlenmesi oldukça önemlidir. Bu çalışmada, ICU’ya kabul edilen (COVID-19 SEVERITY ) ve COVID-19 nedeni ile ölen (COVID-19 MORTALITY ) hastaların bilgilerini içeren veri setleri, Makine Öğrenmesi (ML) yöntemleri kullanılarak COVID-19 prognoz tespiti yapılmıştır. Veri setlerinde bulunan eksik veriler K-En Yakın Komşu (KNN) ile tamamlanmış ve Min-Max normalizasyonu yapılmıştır. Veri setleri, eğitim ve test setleri olarak bölünmüş ve veriler Sentetik Azınlık Aşırı Örnekleme Tekniği (SMOTE) ile dengelenmiştir. Ardından, Kolektif Öğrenme (EL) yöntemleri kullanılarak sınıflandırma gerçekleştirilmiştir. COVID-19 SEVERITY ve COVID-19 MORTALITY için Adaboost sınıflandırıcısı ile sırasıyla %89.54 ve %97.25 başarı elde edilmiştir. ML yöntemleri ile COVID-19 prognozunun başarılı ve hızlı bir şekilde tespit edilmesi, ICU’yu daha verimli kullanmaya ve sağlık sistemlerinin üzerindeki baskıyı hafifletmeye yardımcı olacaktır.

Keywords

Sınıflandırma modelleri, Hematolojik parametreler, Makine öğrenmesi

Detection of COVID-19 Severity and Mortality from Blood Parameters by Ensemble Learning Methods

Abstract

COVID-19 is a pandemic that causes a high rate of spread and Acute Respiratory Distress Syndrome (ARDS). Severe pneumonia in infected individuals has resulted in too many patients being admitted to the Intensive Care Unit (ICU). This has placed unprecedented pressure on health systems by exceeding capacities. It is essential to detect the prognosis of this disease so that the health systems can remain active and the conditions of the patients who need to be hospitalized in the ICU do not become critical. In this study, COVID-19 prognosis was detected by using ICU admission (COVID-19 SEVERITY) and COVID-19 related death (COVID- 19 MORTALITY) datasets with Machine Learning (ML) methods. The missing data of the datasets were filled with K-Nearest Neighbor (KNN), and Min-Max normalization was performed. Datasets were divided three times into training and test sets, and the data were balanced with the Synthetic Minority Oversampling Technique (SMOTE). Then, classification was carried out using Ensemble Learning (EL) methods. For COVID-19 SEVERITY and COVID-19 MORTALITY, 89.54% and 97.25% accuracy were achieved with the Adaboost classifier, respectively. Successful and rapid COVID-19 prognosis detection with ML methods will help to use the ICU more efficiently and relieve the pressure on health systems.

Keywords

Classification models, hematological parameters, machine learning

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