Araştırma Makalesi
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Forecasting of the Dental Workforce with Machine Learning Models

Yıl 2024, Cilt: 6 Sayı: 1, 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

Kaynakça

  • M.A. Myszczynska et al., “Applications of machine learning to diagnosis and treatment of neurodegenerative diseases,” Nat. Rev. Neurol., vol. 16, no. 8, pp. 440–456, Aug. 2020.
  • F. Schwendicke, W. Samek, and J. Krois, “Artificial Intelligence in Dentistry: Chances and Challenges,” J. Dent. Res., vol. 99, no. 7, pp. 769–774, Jul. 2020.
  • M.I. Jordan and T.M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science (80-. )., vol. 349, no. 6245, pp. 255–260, Jul. 2015.
  • A. Atalan, “Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050,” Gümüşhane Üniversitesi Sağlık Bilim. Derg., vol. 9, no. 1, pp. 8–16, Apr. 2020.
  • P. Karmani, A.A. Chandio, I.A. Korejo, and M.S. Chandio, “A Review of Machine Learning for Healthcare Informatics Specifically Tuberculosis Disease Diagnostics,” in Intelligent Technologies and Applications, , pp. 50–61, 2019.
  • K. Shailaja, B. Seetharamulu, and M.A. Jabbar, “Machine Learning in Healthcare: A Review,” in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, pp. 910–914,Mar. 2018.
  • S.S.R. Abidi, P.C.Roy, M.S. Shah, J. Yu, and S. Yan, “A Data Mining Framework for Glaucoma Decision Support Based on Optic Nerve Image Analysis Using Machine Learning Methods,” J. Healthc. Informatics Res., vol. 2, no. 4, pp. 370–401, Dec. 2018.
  • I. Kononenko, “Machine learning for medical diagnosis: history, state of the art and perspective,” Artif. Intell. Med., vol. 23, no. 1, pp. 89–109, Aug. 2001.
  • P. Sajda, “Machıne Learnıng For Detectıon And Dıagnosıs Of Dısease,” Annu. Rev. Biomed. Eng., vol. 8, no. 1, pp. 537–565, Aug. 2006.
  • Z. Liao, D. Li, X. Wang, L. Li, and Q. Zou, “Cancer Diagnosis Through IsomiR Expression with Machine Learning Method,” Curr. Bioinform., vol. 13, no. 1, pp. 57–63, Feb. 2018.
  • A. Atalan, H. Şahin, and Y.A. Atalan, “Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources,” Healthcare, vol. 10, no. 10, p. 1920, Sep. 2022,.
  • A. Atalan, “Effect Of Healthcare Expendıture On The Correlatıon Between The Number Of Nurses And Doctors Employed,” Int. J. Heal. Manag. Tour., vol. 6, no. 2, pp. 515–525, Jul. 2021.
  • Y.E. Ayözen, H. İnaç, A. Atalan, and C.Ç. Dönmez, “E-Scooter Micro-Mobility Application for Postal Service: The Case of Turkey for Energy, Environment, and Economy Perspectives,” Energies, vol. 15, no. 20, p. 7587, Oct. 2022.
  • A. Atalan and A. Keskin, “Estimation of the utilization rates of the resources of a dental clinic by simulation,” Sigma J. Eng. Nat. Sci. – Sigma Mühendislik ve Fen Bilim. Derg., vol. 41, no. 2, pp. 423–432, 2023.
  • K.K. Joshi, K.K. Gupta, and J. Agrawal, “A Review on Application of Machine Learning in Medical Diagnosis,” in 2nd International Conference on Data, Engineering and Applications (IDEA), IEEE, Feb. 2020, pp. 1–6.
  • Y.A. Atalan and A. Atalan, “Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy,” Sustainability, vol. 15, no. 18, p. 13782, Sep. 2023.
  • G. Gunčar et al., “An application of machine learning to haematological diagnosis,” Sci. Rep., vol. 8, no. 1, p. 411, Dec. 2018.
  • A. Atalan, “Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms,” Agribusiness, vol. 39, no. 1, pp. 214–241, Jan. 2023.
  • M. Hung et al., “Development of a recommender system for dental care using machine learning,” SN Appl. Sci., vol. 1, no. 7, p. 785, Jul. 2019.
  • T.H. Farook, N. Bin Jamayet, J.Y. Abdullah, and M.K. Alam, “Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review,” Pain Res. Manag., vol. 2021, pp. 1–9, Apr. 2021.
  • P. Sebastiani, Y.-H. Yu, and M.F. Ramoni, “Bayesian Machine Learning and Its Potential Applications to the Genomic Study of Oral Oncology,” Adv. Dent. Res., vol. 17, no. 1, pp. 104–108, Dec. 2003.
  • D.W. Kim, H. Kim, W. Nam, H.J. Kim, and I.-H. Cha, “Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report,” Bone, vol. 116, pp. 207–214, Nov. 2018.
  • J. Peng, X. Zeng, J. Townsend, G. Liu, Y. Huang, and S. Lin, “A Machine Learning Approach to Uncovering Hidden Utilization Patterns of Early Childhood Dental Care Among Medicaid-Insured Children,” Front. Public Heal., vol. 8, Jan. 2021.
  • J.-H. Lee, D.-H. Kim, S.-N. Jeong, and S.-H. Choi, “Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm,” J. Dent., vol. 77, pp. 106–111, Oct. 2018.
  • J.T. Newton, D. Buck, and D.E. Gibbons, “Workforce planning in dentistry: the impact of shorter and more varied career patterns,” Community Dent. Health, vol. 18, no. 4, p. 236—241, Dec. 2001, [Online].Available:http://europepmc.org/abstract/MED/11789702.
  • P. Harper, E. Kleinman, J. Gallagher, and V. Knight, “Cost‐effective workforce planning: optimising the dental team skill‐mix for England,” J. Enterp. Inf. Manag., vol. 26, no. 1/2, pp. 91–108, Feb. 2013.
  • G. Try, “Too Few Dentists? Workforce Planning 1996–2036,” Prim. Dent. Care, vol. os7, no. 1, pp. 9–13, Jan. 2000.
  • R. Knevel, M. Gussy, and J. Farmer, “Exploratory scoping of the literature on factors that influence oral health workforce planning and management in developing countries,” Int. J. Dent. Hyg., vol. 15, no. 2, pp. 95–105, May 2017.
  • N. Yamalik, E. Ensaldo-Carrasco, and D. Bourgeois, “Oral health workforce planning Part 1 : data available in a sample of FDI member countries,” Int. Dent. J., vol. 63, no. 6, pp. 298–305, Dec. 2013.
  • S. Ahern, N. Woods, O. Kalmus, S. Birch, and S. Listl, “Needs-based planning for the oral health workforce - development and application of a simulation model,” Hum. Resour. Health, vol. 17, no. 1, p. 55, Dec. 2019.
  • J.E. Gallagher, S. Manickam, and N.H. Wilson, “Sultanate of Oman: building a dental workforce,” Hum. Resour. Health, vol. 13, no. 1, p. 50, Dec. 2015.
  • D.N. Teusner, N. Amarasena, J. Satur, S. Chrisopoulos, and D.S. Brennan, “Dental service provision by oral health therapists, dental hygienists and dental therapists in Australia: implications for workforce modelling,” Community Dent Heal., vol. 33, no. 1, pp. 15–22, 2016.
  • R.A. Welikala et al., “Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy,” Comput. Med. Imaging Graph., vol. 43, pp. 64–77, Jul. 2015.
  • Y.V. Srinivasa Murthy and S.G. Koolagudi, “Classification of vocal and non-vocal segments in audio clips using genetic algorithm based feature selection (GAFS),” Expert Syst. Appl., vol. 106, pp. 77–91, Sep. 2018.
  • O.S. Pianykh et al., “Improving healthcare operations management with machine learning,” Nat. Mach. Intell., vol. 2, no. 5, pp. 266–273, May 2020.
  • TUIK, “Sağlık İstatistikleri, istatistiksel Tablolar ve Dinamik Sorgulama,” Türkiye İstatistik Kurumu, 2021. https://tuikweb.tuik.gov.tr/PreTablo.do?alt_id=1095
  • A. Atalan, C.Ç. Dönmez, and Y. Ayaz Atalan, “Yüksek-Eğitimli Uzman Hemşire İstihdamı ile Acil Servis Kalitesinin Yükseltilmesi için Simülasyon Uygulaması: Türkiye Sağlık Sistemi,” Marmara Fen Bilim. Derg., vol. 30, no. 4, pp. 318–338, Dec. 2018.
  • C.R. Vernazza, S. Birch, and N. B. Pitts, “Reorienting Oral Health Services to Prevention: Economic Perspectives,” J. Dent. Res., vol. 100, no. 6, pp. 576–582, Jun. 2021.

Diş Hekimliği İşgücünün Makine Öğrenmesi Modelleri ile Tahmin Edilmesi

Yıl 2024, Cilt: 6 Sayı: 1, 125 - 132, 30.04.2024

Abdulkadir Atalan Hasan Şahin

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

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

  • M.A. Myszczynska et al., “Applications of machine learning to diagnosis and treatment of neurodegenerative diseases,” Nat. Rev. Neurol., vol. 16, no. 8, pp. 440–456, Aug. 2020.
  • F. Schwendicke, W. Samek, and J. Krois, “Artificial Intelligence in Dentistry: Chances and Challenges,” J. Dent. Res., vol. 99, no. 7, pp. 769–774, Jul. 2020.
  • M.I. Jordan and T.M. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science (80-. )., vol. 349, no. 6245, pp. 255–260, Jul. 2015.
  • A. Atalan, “Forecasting for Healthcare Expenditure of Turkey Covering the Years of 2018-2050,” Gümüşhane Üniversitesi Sağlık Bilim. Derg., vol. 9, no. 1, pp. 8–16, Apr. 2020.
  • P. Karmani, A.A. Chandio, I.A. Korejo, and M.S. Chandio, “A Review of Machine Learning for Healthcare Informatics Specifically Tuberculosis Disease Diagnostics,” in Intelligent Technologies and Applications, , pp. 50–61, 2019.
  • K. Shailaja, B. Seetharamulu, and M.A. Jabbar, “Machine Learning in Healthcare: A Review,” in 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), IEEE, pp. 910–914,Mar. 2018.
  • S.S.R. Abidi, P.C.Roy, M.S. Shah, J. Yu, and S. Yan, “A Data Mining Framework for Glaucoma Decision Support Based on Optic Nerve Image Analysis Using Machine Learning Methods,” J. Healthc. Informatics Res., vol. 2, no. 4, pp. 370–401, Dec. 2018.
  • I. Kononenko, “Machine learning for medical diagnosis: history, state of the art and perspective,” Artif. Intell. Med., vol. 23, no. 1, pp. 89–109, Aug. 2001.
  • P. Sajda, “Machıne Learnıng For Detectıon And Dıagnosıs Of Dısease,” Annu. Rev. Biomed. Eng., vol. 8, no. 1, pp. 537–565, Aug. 2006.
  • Z. Liao, D. Li, X. Wang, L. Li, and Q. Zou, “Cancer Diagnosis Through IsomiR Expression with Machine Learning Method,” Curr. Bioinform., vol. 13, no. 1, pp. 57–63, Feb. 2018.
  • A. Atalan, H. Şahin, and Y.A. Atalan, “Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources,” Healthcare, vol. 10, no. 10, p. 1920, Sep. 2022,.
  • A. Atalan, “Effect Of Healthcare Expendıture On The Correlatıon Between The Number Of Nurses And Doctors Employed,” Int. J. Heal. Manag. Tour., vol. 6, no. 2, pp. 515–525, Jul. 2021.
  • Y.E. Ayözen, H. İnaç, A. Atalan, and C.Ç. Dönmez, “E-Scooter Micro-Mobility Application for Postal Service: The Case of Turkey for Energy, Environment, and Economy Perspectives,” Energies, vol. 15, no. 20, p. 7587, Oct. 2022.
  • A. Atalan and A. Keskin, “Estimation of the utilization rates of the resources of a dental clinic by simulation,” Sigma J. Eng. Nat. Sci. – Sigma Mühendislik ve Fen Bilim. Derg., vol. 41, no. 2, pp. 423–432, 2023.
  • K.K. Joshi, K.K. Gupta, and J. Agrawal, “A Review on Application of Machine Learning in Medical Diagnosis,” in 2nd International Conference on Data, Engineering and Applications (IDEA), IEEE, Feb. 2020, pp. 1–6.
  • Y.A. Atalan and A. Atalan, “Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy,” Sustainability, vol. 15, no. 18, p. 13782, Sep. 2023.
  • G. Gunčar et al., “An application of machine learning to haematological diagnosis,” Sci. Rep., vol. 8, no. 1, p. 411, Dec. 2018.
  • A. Atalan, “Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms,” Agribusiness, vol. 39, no. 1, pp. 214–241, Jan. 2023.
  • M. Hung et al., “Development of a recommender system for dental care using machine learning,” SN Appl. Sci., vol. 1, no. 7, p. 785, Jul. 2019.
  • T.H. Farook, N. Bin Jamayet, J.Y. Abdullah, and M.K. Alam, “Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review,” Pain Res. Manag., vol. 2021, pp. 1–9, Apr. 2021.
  • P. Sebastiani, Y.-H. Yu, and M.F. Ramoni, “Bayesian Machine Learning and Its Potential Applications to the Genomic Study of Oral Oncology,” Adv. Dent. Res., vol. 17, no. 1, pp. 104–108, Dec. 2003.
  • D.W. Kim, H. Kim, W. Nam, H.J. Kim, and I.-H. Cha, “Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report,” Bone, vol. 116, pp. 207–214, Nov. 2018.
  • J. Peng, X. Zeng, J. Townsend, G. Liu, Y. Huang, and S. Lin, “A Machine Learning Approach to Uncovering Hidden Utilization Patterns of Early Childhood Dental Care Among Medicaid-Insured Children,” Front. Public Heal., vol. 8, Jan. 2021.
  • J.-H. Lee, D.-H. Kim, S.-N. Jeong, and S.-H. Choi, “Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm,” J. Dent., vol. 77, pp. 106–111, Oct. 2018.
  • J.T. Newton, D. Buck, and D.E. Gibbons, “Workforce planning in dentistry: the impact of shorter and more varied career patterns,” Community Dent. Health, vol. 18, no. 4, p. 236—241, Dec. 2001, [Online].Available:http://europepmc.org/abstract/MED/11789702.
  • P. Harper, E. Kleinman, J. Gallagher, and V. Knight, “Cost‐effective workforce planning: optimising the dental team skill‐mix for England,” J. Enterp. Inf. Manag., vol. 26, no. 1/2, pp. 91–108, Feb. 2013.
  • G. Try, “Too Few Dentists? Workforce Planning 1996–2036,” Prim. Dent. Care, vol. os7, no. 1, pp. 9–13, Jan. 2000.
  • R. Knevel, M. Gussy, and J. Farmer, “Exploratory scoping of the literature on factors that influence oral health workforce planning and management in developing countries,” Int. J. Dent. Hyg., vol. 15, no. 2, pp. 95–105, May 2017.
  • N. Yamalik, E. Ensaldo-Carrasco, and D. Bourgeois, “Oral health workforce planning Part 1 : data available in a sample of FDI member countries,” Int. Dent. J., vol. 63, no. 6, pp. 298–305, Dec. 2013.
  • S. Ahern, N. Woods, O. Kalmus, S. Birch, and S. Listl, “Needs-based planning for the oral health workforce - development and application of a simulation model,” Hum. Resour. Health, vol. 17, no. 1, p. 55, Dec. 2019.
  • J.E. Gallagher, S. Manickam, and N.H. Wilson, “Sultanate of Oman: building a dental workforce,” Hum. Resour. Health, vol. 13, no. 1, p. 50, Dec. 2015.
  • D.N. Teusner, N. Amarasena, J. Satur, S. Chrisopoulos, and D.S. Brennan, “Dental service provision by oral health therapists, dental hygienists and dental therapists in Australia: implications for workforce modelling,” Community Dent Heal., vol. 33, no. 1, pp. 15–22, 2016.
  • R.A. Welikala et al., “Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy,” Comput. Med. Imaging Graph., vol. 43, pp. 64–77, Jul. 2015.
  • Y.V. Srinivasa Murthy and S.G. Koolagudi, “Classification of vocal and non-vocal segments in audio clips using genetic algorithm based feature selection (GAFS),” Expert Syst. Appl., vol. 106, pp. 77–91, Sep. 2018.
  • O.S. Pianykh et al., “Improving healthcare operations management with machine learning,” Nat. Mach. Intell., vol. 2, no. 5, pp. 266–273, May 2020.
  • TUIK, “Sağlık İstatistikleri, istatistiksel Tablolar ve Dinamik Sorgulama,” Türkiye İstatistik Kurumu, 2021. https://tuikweb.tuik.gov.tr/PreTablo.do?alt_id=1095
  • A. Atalan, C.Ç. Dönmez, and Y. Ayaz Atalan, “Yüksek-Eğitimli Uzman Hemşire İstihdamı ile Acil Servis Kalitesinin Yükseltilmesi için Simülasyon Uygulaması: Türkiye Sağlık Sistemi,” Marmara Fen Bilim. Derg., vol. 30, no. 4, pp. 318–338, Dec. 2018.
  • C.R. Vernazza, S. Birch, and N. B. Pitts, “Reorienting Oral Health Services to Prevention: Economic Perspectives,” J. Dent. Res., vol. 100, no. 6, pp. 576–582, Jun. 2021.
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Öğrenme (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Abdulkadir Atalan 0000-0003-0924-3685

Hasan Şahin 0000-0002-8915-000X

Erken Görünüm Tarihi 27 Nisan 2024
Yayımlanma Tarihi 30 Nisan 2024
Gönderilme Tarihi 19 Mart 2024
Kabul Tarihi 16 Nisan 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 6 Sayı: 1

Kaynak Göster

APA Atalan, A., & Şahin, H. (2024). Forecasting of the Dental Workforce with Machine Learning Models. Mühendislik Bilimleri Ve Araştırmaları Dergisi, 6(1), 125-132. https://doi.org/10.46387/bjesr.1455345
AMA Atalan A, Şahin H. Forecasting of the Dental Workforce with Machine Learning Models. Müh.Bil.ve Araş.Dergisi. Nisan 2024;6(1):125-132. doi:10.46387/bjesr.1455345
Chicago Atalan, Abdulkadir, ve Hasan Şahin. “Forecasting of the Dental Workforce With Machine Learning Models”. Mühendislik Bilimleri Ve Araştırmaları Dergisi 6, sy. 1 (Nisan 2024): 125-32. https://doi.org/10.46387/bjesr.1455345.
EndNote Atalan A, Şahin H (01 Nisan 2024) Forecasting of the Dental Workforce with Machine Learning Models. Mühendislik Bilimleri ve Araştırmaları Dergisi 6 1 125–132.
IEEE A. Atalan ve H. Şahin, “Forecasting of the Dental Workforce with Machine Learning Models”, Müh.Bil.ve Araş.Dergisi, c. 6, sy. 1, ss. 125–132, 2024, doi: 10.46387/bjesr.1455345.
ISNAD Atalan, Abdulkadir - Şahin, Hasan. “Forecasting of the Dental Workforce With Machine Learning Models”. Mühendislik Bilimleri ve Araştırmaları Dergisi 6/1 (Nisan 2024), 125-132. https://doi.org/10.46387/bjesr.1455345.
JAMA Atalan A, Şahin H. Forecasting of the Dental Workforce with Machine Learning Models. Müh.Bil.ve Araş.Dergisi. 2024;6:125–132.
MLA Atalan, Abdulkadir ve Hasan Şahin. “Forecasting of the Dental Workforce With Machine Learning Models”. Mühendislik Bilimleri Ve Araştırmaları Dergisi, c. 6, sy. 1, 2024, ss. 125-32, doi:10.46387/bjesr.1455345.
Vancouver Atalan A, Şahin H. Forecasting of the Dental Workforce with Machine Learning Models. Müh.Bil.ve Araş.Dergisi. 2024;6(1):125-32.
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