Forecasting of the Dental Workforce with Machine Learning Models

Yıl 2024, , 125 - 132, 30.04.2024

Abdulkadir Atalan , Hasan Şahin

https://doi.org/10.46387/bjesr.1455345

Öz

The aim of this study is to determine the factors affecting the dental workforce in Turkey to estimate the dentists employed with machine learning models. The predicted results were obtained by applying machine learning methods; namely, generalized linear model (GLM), deep learning (DL), decision tree (DT), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM) were compared. The RF model, which has a high correlation value (R2=0.998) with the lowest error rate (RMSE=656.6, AE=393.1, RE=0.025, SE=496115.7), provided the best estimation result. The SVM model provided the worst estimate data based on the values of the performance measurement criteria. This study is the most comprehensive in terms of the dental workforce, which is among the healthcare resources. Finally, we present an example of future applications for machine learning models that will significantly impact dental healthcare management.

Anahtar Kelimeler

Dental Workforce, Machine Learning, Forecasting, Healthcare Management

Diş Hekimliği İşgücünün Makine Öğrenmesi Modelleri ile Tahmin Edilmesi

Öz

Bu çalışmanın amacı, Türkiye'deki diş hekimleri işgücünü etkileyen faktörleri belirleyerek makine öğrenimi modelleri kullanarak istihdam edilen diş hekimlerini tahmin etmektir. Makine öğrenimi yöntemleri olan genelleştirilmiş lineer model (GLM), derin öğrenme (DL), karar ağacı (DT), rastgele orman (RF), gradyan artırılmış ağaçlar (GBT) ve destek vektör makinesi (SVM) uygulanarak tahmin edilen sonuçlar karşılaştırıldı. En yüksek korelasyon değerine (R2=0.998) ve en düşük hata oranına (RMSE=656.6, AE=393.1, RE=0.025, SE=496115.7) sahip olan RF modeli, en iyi tahmin sonucunu sağlamıştır. SVM modeli, performans ölçütü değerlerine dayanarak en kötü tahmin verilerini sağlamıştır. Bu çalışma, sağlık kaynakları arasında olan diş hekimleri işgücü açısından en kapsamlı olanıdır. Son olarak, diş sağlığı yönetimini önemli ölçüde etkileyecek makine öğrenimi modelleri için gelecekteki uygulamaların bir örneği sunulmuştur.

Anahtar Kelimeler

Diş Hekimleri İşgücü, Makine Öğrenimi, Tahmin, Sağlık Yönetimi

Kaynakça

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