Covid-19'un Yayılım Tahminine Yönelik Makine Öğrenmesi ve Derin Öğrenme Tabanlı Karşılaştırmalı Bir Analiz: Türkiye İçin Örnek Bir Çalışma

Year 2022, Volume: 34 Issue: 2, 709 - 717, 30.09.2022

Anıl Utku , Ümit Can

https://doi.org/10.35234/fumbd.1125609

Abstract

Salgınlar tarih boyunca çeşitli zaman dilimlerinde ortaya çıkmış ve insan topluluklarına ciddi zararlar vermiştir. Günümüzde ise bu salgınların modern versiyonu Covid-19 milyonlarca insanın hayatını kaybetmesine ve bir o kadarının da sağlık sorunları yaşamasına yol açmıştır. Tüm dünya, altyapı, finans, veri kaynakları, koruyucu donanımlar, hayati risk tedavileri ve diğer birçok kaynak açısından bu ölümcül hastalığın yayılmasına karşı mücadele etmek için inanılmaz çaba sarf etmektedir. Araştırmacılar ülke çapında paylaşılan verileri kullanarak bu salgın durumunu analiz etmek için matematiksel modeller geliştirmektedirler. Ülkeler salgın hızına bağlı olarak bu salgınla mücadele etmeye çalışmaktadırlar. Bu çalışmada Türkiye özelinde vaka ve ölüm sayılarının tahmin edilmesi için LSTM tabanlı bir tahmin modeli oluşturulmuştur. Bu modelin tahmin başarısını ölçmek için RF, SVM, XGBoost, MLP, CNN ve RNN olmak üzere popüler derin öğrenme yöntemleri dâhil altı makine öğrenmesi yöntemi kullanılmıştır. LSTM modeli vaka sayılarının tahmininde, MSE: 16670823,040 RMSE: 4082,991 MAE: 2543,651 R2: 0,975 sonuçlarını ve ölüm sayılarının tahmininde ise MSE: 331,351 RMSE: 18,203 MAE: 14,891 R2: 0,740 sonuçlarını alarak en başarılı model olmuştur.

Keywords

Covid-19, makine öğrenmesi, derin öğrenme, vaka tahmini

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