Calculation of Mortality Risks of Covid-19 Patients with Machine Learning Algorithms

Year 2022, , 994 - 1011, 22.08.2022

Mehmet Aziz Çakmak , Mehmet Emin Kurt , Cuma Çakmak

https://doi.org/10.21076/vizyoner.1074212

Cited By: 2

Abstract

The aim of the study is to calculate the mortality risk of patients diagnosed with Covid-19 using Machine Learning algorithms. In the study, demographic and clinical data of patients admitted to the health facility with the diagnosis of Covid-19 in Atlanta, Georgia, which are published as open access on the web, are used. The mortality risk of the patients is calculated using Machine Learning algorithms called Decision Tree, Random Forest and Adaptive Boost based on the data. It is observed that the demographic and clinical findings of the patients are effective on mortality risks and that the Machine Learning-based prediction modeling created in this direction can be applied with high reliability (Acc=83.5). With the findings obtained, high-reliability classification models can be created using Machine Learning methods and decision support modules can be created that can guide clinicians and health professionals in patient prioritization in line with the calculation of mortality risks of patients. By creating web-based modules, a scientific basis is established for health authorities, clinicians and hospital managers to make effective and efficient preparations for bed occupancy planning. Unnecessary health expenditures and patients who are likely to have a relatively mild illness can be prevented from receiving unnecessary treatment.

Keywords

Machine Learning Algorithms/Covid-19, Mortality Risk, Clinical Data, Clinicians

Makine Öğrenmesi Algoritmaları ile Covid-19 Hastalarının Mortalite Risklerinin Hesaplanması

Abstract

Bu çalışmanın amacı, Makine Öğrenmesi algoritmalarıyla Covid-19 tanılı hastaların mortalite riskinin hesaplanmasıdır. Bu çalışmada, web üzerinde açık erişimli olarak yayınlanan Atlanta, Georgia’da Covid-19 tanısı alarak sağlık tesisine yatan hastaların demografik ve klinik verileri kullanılmıştır. Bu veriler üzerinden Karar Ağacı, Rastsal Orman ve Adaptive Boost olarak adlandırılan Makine Öğrenmesi algoritmaları kullanılarak hastaların mortalite riski hesaplanmıştır. Hastaların demografik ve klinik bulgularının mortalite riskleri üzerinde etkili olduğu ve bu doğrultuda oluşturulan Makine Öğrenmesi tabanlı tahmin modellemesinin yüksek güvenirlikle (Acc=83,5) uygulanabileceği görülmüştür. Elde edilen bulgularla birlikte Makine Öğrenmesi yöntemleri kullanılarak yüksek düzeyde güvenilir sınıflandırma modellemeleri oluşturulup hastaların mortalite risklerinin hesaplanması doğrultusunda klinisyenler ve sağlık profesyonellerine hasta önceliklendirme konusunda kılavuz olabilecek karar destek modülleri oluşturulabilmektedir. Web tabanlı modüller oluşturularak sağlık otoritelerine, klinisyenlere ve hastane yöneticilerine yatak doluluğu planlaması açısından etkin ve verimli hazırlık yapabilmeleri açısından bilimsel dayanak oluşturulmaktadır. Gereksiz sağlık harcamalarının ve hastalığı görece hafif geçirme ihtimali olan hastaların gereksiz tedavi almaları önlenebilecektir.

Keywords

Makine Öğrenmesi Algoritmaları, Covid-19, Mortalite Riski, Klinik Veriler, Klinisyenler

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