Hibrit FUCOM-Pareto analizi-rastgele orman yöntemi kullanılarak COVID-19 onaylanmış vaka sayısının tahmin edilmesi

Abstract

Aralık 2019 tarihinde ortaya çıkan ve halen etkisini devam ettiren COVID-19 salgınının ardından neredeyse tüm ülkeler virüsün yayılmasını kontrol altına almak için katı önlemler uygulamak zorunda kalmıştır. COVID-19’un yayılım hızına etki eden çok sayıda kriter olması ve en etkili kriterlerin belirlenememesi yayılımın, dolayısıyla pozitif vaka ve ölüm sayısının artmasına neden olmaktadır. Uzmanların yayılımı azaltabilmesi yayılımı etkileyen kriterlerin belirlenmesine bağlıdır. Bu nedenle çalışmada; öncelikle yayılım hızına etki eden kriterlere ait ağırlıklar çok kriterli karar verme yöntemi olan tam tutarlılık yöntemi (FUCOM) kullanılarak belirlenmiş, elde edilen kriter ağırlıkları baz alınarak yayılımı en çok etkileyen kriterler Pareto analizi ile tespit edilmiştir. Daha sonra elde edilen kriter baz alınarak Rastgele Orman (RO) yöntemiyle onaylanmış vaka sayıları tahmin edilmiştir. RO yöntemine ait performans kriterleri değerleri; yapay sinir ağı, karar ağacı ve destek vektör makinası gibi farklı yapay zeka yöntemleri ile karşılaştırılmıştır. RO yönteminin; RMSE (3247), MAE (1714) ve RRSE (0.374) hata değerleriyle ve %92.9 gibi yüksek tahmin başarısı ile daha iyi değerler verdiği görülmüştür.

Keywords

• COVID-19
• Tam tutarlılık yöntemi
• Pareto analizi
• rastgele Orman
• Yapay zekâ yöntemleri

Prediction of the number of COVID-19 confirmed cases using the hybrid FUCOM-Pareto analysis- random forest method

Abstract

After the COVID-19 epidemic, which emerged in December 2019 and is still in effect, almost all countries had to implement strict measures to control the spread of the virus. The ability of experts to reduce the spread primarily depends on the determination of the criteria affecting the spread. The fact many criteria that affect the rate of spread of COVID-19 and the most effective criteria cannot be determined, causes the spread, and therefore the number of positive cases and deaths to increase. Therefore, in the study; firstly, the weights of the criteria affecting the rate of spread were determined by using the full consistency method (FUCOM), which is a multi-criteria decision-making method, and the criteria that most affected the spread were determined by Pareto analysis, based on the criteria weights obtained. Then, based on the criteria obtained, the number of confirmed cases was predicted using the random forest method. The performance criteria values of the random forest were compared with different artificial intelligence methods such as artificial neural network, decision tree and support vector machine. Random forest gave the best results with error values (RMSE (3247), MAE (1714) and RRSE (0.374)). In addition, the random forest achieved a high prediction success of 92.9%.

Keywords

• COVID-19
• Full Consistency method
• Pareto analysis
• Random forest
• Artificial intelligence methods

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