COVID19PREDICTOR: KLİNİK VERİLERE VE RUTİN TESTLERE DAYALI OLARAK COVID-19 TEŞHİSİ İÇİN MAKİNE ÖĞRENİMİ MODELLERİ GELİŞTİRMEYE YARAYAN WEB TABANLI ARAYÜZ

Year 2022, Volume: 3 Issue: 3, 216 - 221, 31.12.2022

Volkan Kapucu , Sultan Turhan , Metin Pıçakçıefe , Eralp Doğu

https://doi.org/10.52831/kjhs.1117894

Abstract

Amaç: Covid-19 salgını sağlıkla ilgili, sosyal, ekonomik ve bireysel etkiler nedeniyle birçok ülkenin birincil sağlık sorunu haline gelmiştir. Salgın tahmin modellerinin geliştirilmesinin yanı sıra hastalığın risk faktörlerinin incelenmesi ve teşhise yönelik modellerin geliştirilmesi büyük önem taşımaktadır. Bu çalışma, rutin laboratuvar test sonuçları, risk faktörleri, birlikte var olan sağlık koşullarına ilişkin bilgiler gibi klinik verilere dayalı olarak Covid-19'u teşhis etmek için makine öğrenimi yaklaşımlarının kullanıldığı bir iş akışı olan Covid19PredictoR arayüzünü tanıtmaktadır.
Yöntem: Covid19PredictoR arayüzü, R/Shiny'de (https://biodatalab.shinyapps.io/Covid19PredictoR/) açık kaynaklı web tabanlı bir arayüzdür. Sistem içerisinde lojistik regresyon, C5.0, karar ağacı, rastgele orman ve XGBoost modelleri geliştirilebilir. Bu modeller aynı zamanda tahmin amacıyla da kullanılabilir. Model geliştirme sırasında ek olarak tanımlayıcı istatistikler, veri ön işleme ve model ayarlama adımları sağlanır.
Bulgular: Einsteindata4u veri seti, Covid19PredictoR arayüzü ile analiz edildi. Bu örnekle, arayüzün eksiksiz çalışması ve iş akışının tüm adımlarının gösterimi aktarıldı. Veri seti için yüksek performanslı makine öğrenme modelleri geliştirilmiş ve tahmin için en iyi modeller kullanıldı. Model başına vaka için özelliklerin analizi ve görselleştirilmesi (yaş, kabul verileri ve laboratuvar testleri) yapıldı.
Sonuç: Covid-19 hastalığını, ilgili risk faktörleri açısından değerlendirmek için makine öğrenimi algoritmalarının kullanımı, hızla artmaktadır. Bu algoritmaların çeşitli platformlarda uygulanması, uygulama zorlukları, tekrarlanabilirlik ve tekrar üretilebilirlik sorunları yaratmaktadır. Arayüz ile standart bir iş akışına dönüştürülen, tasarlanmış bu işlem zinciri, çeşitli geçmiş deneyimlere sahip sağlık uzmanlarının rahatlıkla kullanabileceği ve raporlayabileceği kullanıcı dostu bir yapı sunar.

Keywords

Covid-19, Makine Öğrenmesi, Shiny, Arayüz

COVID19PREDICTOR: WEB-BASED INTERFACE TO DEVELOP MACHINE LEARNING MODELS FOR DIAGNOSIS OF COVID-19 BASED ON CLINICAL DATA AND ROUTINE TESTS

Abstract

Objective: The Covid-19 outbreak has become the primary health problem of many countries due to health related, social, economic and individual effects. In addition to the development of outbreak prediction models, the examination of risk factors of the disease and the development of models for diagnosis are of high importance. This study introduces the Covid19PredictoR interface, a workflow where machine learning approaches are used for diagnosing Covid-19 based on clinical data such as routine laboratory test results, risk factors, information on co-existing health conditions.
Method: Covid19PredictoR interface is an open source web based interface on R/Shiny (https://biodatalab.shinyapps.io/Covid19PredictoR/). Logistic regression, C5.0, decision tree, random forest and XGBoost models can be developed within the framework. These models can also be used for predictive purposes. Descriptive statistics, data pre-processing and model tuning steps are additionally provided during model development.
Results: Einsteindata4u dataset was analyzed with the Covid19PredictoR interface. With this example, the complete operation of the interface and the demonstration of all steps of the workflow have been shown. High performance machine learning models were developed for the dataset and the best models were used for prediction. Analysis and visualization of features (age, admission data and laboratory tests) were carried out for the case per model.
Conclusion: The use of machine learning algorithms to evaluate Covid-19 disease in terms of related risk factors is rapidly increasing. The application of these algorithms on various platforms creates application difficulties, repeatability and reproducibility problems. The proposed pipeline, which has been transformed into a standard workflow with the interface, offers a user-friendly structure that healthcare professionals with various background can easily use and report.

Keywords

Covid-19, Machine Learning, Shiny, Interface

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