Makine Öğrenmesi Yaklaşımlarını Kullanarak Salgınları Erken Evrede Tespit Etme Alanındaki Eğilimler

Year 2021, Volume: 14 Issue: 4, 355 - 366, 31.10.2021

Ali Şenol Yavuz Canbay Mahmut Kaya

Abstract

Tüm dünyayı etkisi altına alan COVID-19, salgınları erken dönemde tespit etmeye çalışan çalışmaların önemini ortaya koymaktadır. Herhangi bir salgın erken aşamada tespit edilebilirse, hastalığa yakalanan kişi sayısını azaltabilir ve gerekli tedavi daha erken sürede bulunabilir ve ek olarak tedavi masrafları da azaltılabilir. Salgınların erken aşamada tespit edilmesini sağlayan en önemli veri işleme yaklaşımlarından makine öğrenmesi, yeni gelen verileri, olayı veya durumu tahmin etmek için matematiksel modelleri ve istatistiksel yöntemleri kullanır. Makine öğrenmesi yaklaşımlarıyla, tıbbi veriler analiz edilerek ve işlenerek hastalıklar hakkında tahminlerde bulunulabilir. Çünkü daha önce toplanan hasta verileri, makine öğrenmesi yöntemleri kullanarak hastalıkların teşhis edilmesine imkân sağlayabilir. Hastalıkların yanı sıra, daha önce toplanan veriler kullanılarak salgınlar hakkında da tahminlerde bulunulabilir. Daha önce ortaya çıkan salgınların yeniden ortaya çıkışını tahmin etmek için denetimli öğrenme yaklaşımları olan Naive Bayes, Destek Vektör Makineleri (DVM), Karar Ağaçları (KA), Rastgele Orman (RO) ve Yapay Sinir Ağları (YSA) gibi birçok yaklaşım olsa da, temel bileşenler ve kümeleme analizi gibi denetimsiz öğrenme yaklaşımları da kullanılarak daha önce benzeri görülmemiş salgınlar tespit edilebilir. Bu çalışmada, bu alanda çalışmak isteyen araştırmacılara ışık tutmak amacıyla salgınları tespit etmeye yönelik geliştirilmiş olan makine öğrenmesi yaklaşımlarının ayrıntılı bir analizi sunulmaktadır.

Keywords

makine öğrenmesi, salgın erken tespiti, pandemi, tahmin

Trends in Outbreak Detection in Early Stage by Using Machine Learning Approaches

Abstract

COVID-19 pandemic affecting the whole world, reveals the importance of the studies that trying to detect the outbreaks in early stage. If any outbreak can be detected in an early stage, the number of infected people can be reduced, the necessary treatment can be found and treatment expenses can be also reduced. The most important data processing approaches enabling to detect outbreaks in an early stage are machine learning approaches, which use mathematical models and statistical background. With machine learning techniques, medical data can be analyzed and processed to make predictions of illnesses. Because, previously collected patient datasets help to perform these predictions. Beside illnesses, outbreaks can be also predicted by using these collected datasets. Machine learning techniques enable us to process labelled and unlabelled datasets with the help of supervised and unsupervised approaches, respectively. Although there are many supervised learning approaches like Naïve Bayes (NB), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF) and Artificial Neural Networks (ANN) to predict the emergence of the outbreaks that appeared before, it is also possible to detect any outbreak which are unprecedented before by using unsupervised learning approaches like principal component and cluster analysis. In this study, it is aimed to present a detailed analysis of machine learning approaches in outbreak detecting area to give a lead to the researchers who want to work in this area.

Keywords

machine learning, outbreak early detection, pandemic, prediction

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