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Tiroit kanseri hastalık tanısında lojistik regresyon kullanımı

Yıl 2024, Cilt: 39 Sayı: 3, 1509 - 1524, 20.05.2024
https://doi.org/10.17341/gazimmfd.1253193

Öz

Tiroit kanseri, 2020'de elde edilen sonuçlara göre tüm kanserlerin küresel insidansının %3'üne karşılık gelmektedir. Bazı yüksek ve orta gelirli ülkelerde tiroit kanseri insidansı son 30 yılda önemli ölçüde artmıştır.
Tiroit nodülü, tiroit bezinin içinde kendisini çevreleyen tiroit parankiminden radyolojik olarak ayırt edilebilen bir lezyondur. Erişkinlerin yaklaşık %60'ında bir veya daha fazla tiroit nodülü bulunur. Tiroitte kanser olasılığı önemli endişe kaynağıdır. Tiroit nodüllerine yaklaşımda fizik muayene, anomnezi, serum tiroit fonksiyon testleri, ultrasonografi (USG) kullanılır. USG saptanan nodüller 1 cm’den büyük ve malignite açısından kuşkuluysa ince iğne aspirasyon (İİA) biyopsisi kullanılır ve değerlendirmeler yapılır.
İyi huylu İİA sonuçları gereksiz tiroit ameliyatlarının önlenmesine yardımcı olur. Malign hücreler tespit edilirse, İİA sonucu cerrahi stratejinin elde edilmesinde belirleyici bir faktördür. Buna rağmen cerrahlar malign potansiyeline ilişkin belirsizlik nedeniyle çok yüksek oranda benign tiroit dokusu rezeke etmektedir. Bu nedenle daha doğru sonuçlar veren non-invaziv tekniklere ihtiyaç duyulmaktadır. Bu çalışmanın amacı, tiroit dokusu çok fazla rezeke edilmeden önce, önceki hasta verileri üzerinden Makine öğrenmesi metotları kullanılarak tanının kesine yakın elde edilmesidir. Bu çalışma ile hastaların kan testlerini, USG, IIA biyopsisi sonuçlarını kullanarak nodülün malignitesini tahmin eden bir model üzerinde çalıştık. Model için kullanılan eldeki hasta verileri ameliyat sonrası kesin sonuçları içermekte ve sonuçlar binominal veri olarak gösterilmektedir. Tiroit kanseri olma olasılığı için en iyi tahmin sonucunu %99,31 olasılık ile makine öğrenmesi metotlarından biri olan Lojistik regresyon tekniği vermiştir.

Kaynakça

  • 1. Niederhuber J.E., Armitage J.O., Doroshow J.H., Kastan M.B., Tepper J.E., Abeloffs Clinical Oncology, 6th edition, Elsevier Publishing, Philadelphia, A.B.D., 2020.
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  • 7. Li M., Brito J.P., Vaccarella S., Long-term declines of thyroid cancer mortality: an international age–period–cohort analysis, Thyroid 30 (6), 838-846, 2020.
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  • 9. Feldkamp J., Führer D., Luster M., Musholt T.J., Spitzweg C, Schott M., Fine Needle Aspiration in the Investigation of Thyroid Nodules, Deutsches Arzteblatt international, 113 (20), 353-362, 2016.
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  • 14. Larosa E., Danks D., Impacts on Trust of Healthcare AI Roles for Healthcare AI, AIES '18: Proceedings of the 2018 AAAI/ACM Conference on AI, New Orleans, ABD, 210-2015, 2018.
  • 15. Luca M., Kleinberg J., Mullainathan S., Algorithms Need Managers Too. Harvard Business Review. https://hbr.org/2016/01/algorithms-need-managers-too. Yayın tarihi Ocak-Şubat, 2016. Erişim tarihi Haziran 16, 2020.
  • 16. Hossen S., Big Data And Pattern Recognition, Machine Learning and Big Data Concepts Algorithms Tools and Applications, Dulhare N.U., Scrivener Publishing LLC, 1, 73-103, 2020.
  • 17. Forman G., An Extensive Empirical Study of Feature Selection Metrics for Text Classification, Journal of Machine Learning Research, 3, 1289–1305, 2003.
  • 18. Liu T., Liu S., Chen, Z., Ma W.Y., An Evaluation on Feature Selection for Text Clustering, Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington D.C, A.B.D., 488-495, 21-24 August, 2003.
  • 19. Bolon V.C., Sanchez N.M., Alonso A.B., Benitez J.M., Herrera F., A review of microarray datasets and applied feature selection methods, Information Sciences, 282, 111–135, 2014.
  • 20. Pouramirarsalani A., Khalilian M., Nikravanshalmani A., Fraud detection in E-banking by using the hybrid feature selection and evolutionary algorithms, IJCSNS, 17(8), 271- 279, 2017.
  • 21. Wang D., Zhang Z., Bai R., Mao Y., A hybrid system with filter approach and multiple population genetic algorithm for feature selection in credit scoring, Journal of Computational and Applied Mathematics, 329, 307-321, 2017.
  • 22. Subramanya K. B., Somani A., Enhanced feature mining and classifier models to predict customer churn for an E-retailer, Cloud Computing Data Science & Engineering Confluence, 7th International Conference, Noida-Hindistan, 531-536, 12-13 January, 2017.
  • 23. Mohamad M., Selamat A., An evaluation on the efficiency of hybrid feature selection in spam email classification, Computer, Communications and Control Technology (I4CT) 2015 International Conference, Kuching-Malezya, 227-231, 21-23 April, 2015.
  • 24. Ananthakumar U., Sarkar R., Application of Logistic Regression in Assessing Stock Performances, IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, Pervasive Intelligence and Computing, Big Data Intelligence and Computing and Cyber Science and Technology Congress, Orlando-A.B.D., 1242-1247, 06-10 November, 2017.
  • 25. Joseph S., Munn Brent A., Lanting Steven J., MacDonald Lyndsay E., Somerville Jacquelyn D., Marsh Dianne M., Bryant Bert M., Logistic Regression and Machine Learning Models Cannot Discriminate Between Satisfied and Dissatisfied Total Knee Arthroplasty Patients, The Journal of Arthroplasty, 37, 267-273, 2022.
  • 26. Rossi R., Socci V., Talevi D., Mensi S., Niolu C., Pacitti F., Di Marco A., Rossi A., Siracusano A., Di Lorenzo G., COVID-19 pandemic and lockdown measures impact on mental health among the general population in Italy, Front Psychiatry, 11 (790), 125-132, 2020.
  • 27. Alfawaz H., Yakout S.M., Wani K., Aljumah G.A., Ansari M.G.A., Khattak M.N.K., Hussain S.D., Al-Daghri N.M., Dietary intake and mental health among Saudi adults during COVID-19 lockdown, International Journal of Environmental Research and Public Health, 18 (4), 153-165, 2021.
  • 28. Ting D.S.J., Krause S., Said D.G., Dua H.S., Psychosocial impact of COVID-19 pandemic lockdown on people living with eye diseases in the UK, Eye, 35 (7), 2064–2066, 2021.
  • 29. Jacob L., Smith L., Armstrong N. C., Yakkundi A., Barnett Y., Butler L., Tully M. A., Alcohol use and mental health during COVID-19 lockdown: A cross-sectional study in a sample of UK adults, Drug and alcohol dependence, 219, 186-198, 2021.
  • 30. Fu W., Yan S., Zong Q., Luxford D.A., Song X., Lv Z., Lv C., Mental health of college students during the COVID-19 epidemic in China, The Journal of Affective Disorders, 280, 7–10, 2021.
  • 31. Jiang W., Liu X., Zhang J., Feng Z., Mental health status of Chinese residents during the COVID-19 epidemic, BMC Psychiatry, 20 (1), 1–14, 2020.
  • 32. Liu Z., Liu R., Zhang Y., Zhang R., Liang L., Wang Y., Wei Y., Zhu R., Wang F., Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic, BMC Psychiatry, 21 (1), 498-509, 2021.
  • 33. Liu Y., Chen H., Zhang N., Wang X., Fan Q., Zhang Y., Huang L., Hu B.O., Li M., Anxiety and depression symptoms of medical staff under COVID-19 epidemic in China, The Journal of Affective Disorders, 278, 144–148, 2021.
Yıl 2024, Cilt: 39 Sayı: 3, 1509 - 1524, 20.05.2024
https://doi.org/10.17341/gazimmfd.1253193

Öz

Kaynakça

  • 1. Niederhuber J.E., Armitage J.O., Doroshow J.H., Kastan M.B., Tepper J.E., Abeloffs Clinical Oncology, 6th edition, Elsevier Publishing, Philadelphia, A.B.D., 2020.
  • 2. National Library of Medicine. MedLinePlus. Thyroid Cancer. https://medlineplus.gov/thyroidcancer.html, Erişim tarihi Temmuz 18, 2023.
  • 3. Ferlay J., Ervik M., Lam F., Global Cancer Observatory: Cancer Today, International Agency for Research on Cancer, Lyon, France, 2020.
  • 4. Lortet T. J., Franceschi S., Dal M.L., Vaccarella S., Thyroid cancer “epidemic” also occurs in low- and middle-income countries, International Journal of Cancer (IJC), 144 (9), 2082-2087, 2019.
  • 5. Li M., Dal Maso L., Vaccarella S., Global trends in thyroid cancer incidence and the impact of overdiagnosis, Lancet Diabetes Endocrinol, 8 (6), 468-470, 2020.
  • 6. Grani G., Sponziello M., Pecce V., Ramundo V., Durante C., Contemporary Thyroid Nodule Evaluation and Management. The Journal of Clinical Endocrinol Metabolism, 105 (9), 2869-2883, 2020.
  • 7. Li M., Brito J.P., Vaccarella S., Long-term declines of thyroid cancer mortality: an international age–period–cohort analysis, Thyroid 30 (6), 838-846, 2020.
  • 8. Grani G., Sponziello M., Pecce V., Ramundo V., Durante C., Contemporary Thyroid Nodule Evaluation and Management, The Journal of Clinical Endocrinol Metabolism, 105 (9), 2869-2883, 2020.
  • 9. Feldkamp J., Führer D., Luster M., Musholt T.J., Spitzweg C, Schott M., Fine Needle Aspiration in the Investigation of Thyroid Nodules, Deutsches Arzteblatt international, 113 (20), 353-362, 2016.
  • 10. Le A.R., Thompson G.W., Hoyt B.J., Thyroid Fine-needle aspiration biopsy: an evaluation of its utility in a community setting, The Journal of Otolaryngol Head Neck Surgery, 44 (1), 12-23, 2015.
  • 11. Fett M.J, Technology, Health and Health Care, 5, Department of Health and Aged Care, Canberra, Australia, 2000.
  • 12. Saluvan M., The Role of Information Systems in Improving the Quality of Health Services, Journal of Health Sciences, 2 (1), 25–39, 2013.
  • 13. Coltin K. L., Using Information Technology to Improve the Quality of Health Care, Health Care Online, 272 (23), 123–158, 1995.
  • 14. Larosa E., Danks D., Impacts on Trust of Healthcare AI Roles for Healthcare AI, AIES '18: Proceedings of the 2018 AAAI/ACM Conference on AI, New Orleans, ABD, 210-2015, 2018.
  • 15. Luca M., Kleinberg J., Mullainathan S., Algorithms Need Managers Too. Harvard Business Review. https://hbr.org/2016/01/algorithms-need-managers-too. Yayın tarihi Ocak-Şubat, 2016. Erişim tarihi Haziran 16, 2020.
  • 16. Hossen S., Big Data And Pattern Recognition, Machine Learning and Big Data Concepts Algorithms Tools and Applications, Dulhare N.U., Scrivener Publishing LLC, 1, 73-103, 2020.
  • 17. Forman G., An Extensive Empirical Study of Feature Selection Metrics for Text Classification, Journal of Machine Learning Research, 3, 1289–1305, 2003.
  • 18. Liu T., Liu S., Chen, Z., Ma W.Y., An Evaluation on Feature Selection for Text Clustering, Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington D.C, A.B.D., 488-495, 21-24 August, 2003.
  • 19. Bolon V.C., Sanchez N.M., Alonso A.B., Benitez J.M., Herrera F., A review of microarray datasets and applied feature selection methods, Information Sciences, 282, 111–135, 2014.
  • 20. Pouramirarsalani A., Khalilian M., Nikravanshalmani A., Fraud detection in E-banking by using the hybrid feature selection and evolutionary algorithms, IJCSNS, 17(8), 271- 279, 2017.
  • 21. Wang D., Zhang Z., Bai R., Mao Y., A hybrid system with filter approach and multiple population genetic algorithm for feature selection in credit scoring, Journal of Computational and Applied Mathematics, 329, 307-321, 2017.
  • 22. Subramanya K. B., Somani A., Enhanced feature mining and classifier models to predict customer churn for an E-retailer, Cloud Computing Data Science & Engineering Confluence, 7th International Conference, Noida-Hindistan, 531-536, 12-13 January, 2017.
  • 23. Mohamad M., Selamat A., An evaluation on the efficiency of hybrid feature selection in spam email classification, Computer, Communications and Control Technology (I4CT) 2015 International Conference, Kuching-Malezya, 227-231, 21-23 April, 2015.
  • 24. Ananthakumar U., Sarkar R., Application of Logistic Regression in Assessing Stock Performances, IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, Pervasive Intelligence and Computing, Big Data Intelligence and Computing and Cyber Science and Technology Congress, Orlando-A.B.D., 1242-1247, 06-10 November, 2017.
  • 25. Joseph S., Munn Brent A., Lanting Steven J., MacDonald Lyndsay E., Somerville Jacquelyn D., Marsh Dianne M., Bryant Bert M., Logistic Regression and Machine Learning Models Cannot Discriminate Between Satisfied and Dissatisfied Total Knee Arthroplasty Patients, The Journal of Arthroplasty, 37, 267-273, 2022.
  • 26. Rossi R., Socci V., Talevi D., Mensi S., Niolu C., Pacitti F., Di Marco A., Rossi A., Siracusano A., Di Lorenzo G., COVID-19 pandemic and lockdown measures impact on mental health among the general population in Italy, Front Psychiatry, 11 (790), 125-132, 2020.
  • 27. Alfawaz H., Yakout S.M., Wani K., Aljumah G.A., Ansari M.G.A., Khattak M.N.K., Hussain S.D., Al-Daghri N.M., Dietary intake and mental health among Saudi adults during COVID-19 lockdown, International Journal of Environmental Research and Public Health, 18 (4), 153-165, 2021.
  • 28. Ting D.S.J., Krause S., Said D.G., Dua H.S., Psychosocial impact of COVID-19 pandemic lockdown on people living with eye diseases in the UK, Eye, 35 (7), 2064–2066, 2021.
  • 29. Jacob L., Smith L., Armstrong N. C., Yakkundi A., Barnett Y., Butler L., Tully M. A., Alcohol use and mental health during COVID-19 lockdown: A cross-sectional study in a sample of UK adults, Drug and alcohol dependence, 219, 186-198, 2021.
  • 30. Fu W., Yan S., Zong Q., Luxford D.A., Song X., Lv Z., Lv C., Mental health of college students during the COVID-19 epidemic in China, The Journal of Affective Disorders, 280, 7–10, 2021.
  • 31. Jiang W., Liu X., Zhang J., Feng Z., Mental health status of Chinese residents during the COVID-19 epidemic, BMC Psychiatry, 20 (1), 1–14, 2020.
  • 32. Liu Z., Liu R., Zhang Y., Zhang R., Liang L., Wang Y., Wei Y., Zhu R., Wang F., Latent class analysis of depression and anxiety among medical students during COVID-19 epidemic, BMC Psychiatry, 21 (1), 498-509, 2021.
  • 33. Liu Y., Chen H., Zhang N., Wang X., Fan Q., Zhang Y., Huang L., Hu B.O., Li M., Anxiety and depression symptoms of medical staff under COVID-19 epidemic in China, The Journal of Affective Disorders, 278, 144–148, 2021.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Mehmet Emin Asan 0000-0003-4948-0116

Harun Taşkın 0000-0002-5542-0374

Murat Alemdar 0000-0001-7127-3119

Recayi Capoglu 0000-0003-4438-4301

Erken Görünüm Tarihi 19 Ocak 2024
Yayımlanma Tarihi 20 Mayıs 2024
Gönderilme Tarihi 19 Şubat 2023
Kabul Tarihi 11 Ağustos 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 39 Sayı: 3

Kaynak Göster

APA Asan, M. E., Taşkın, H., Alemdar, M., Capoglu, R. (2024). Tiroit kanseri hastalık tanısında lojistik regresyon kullanımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 39(3), 1509-1524. https://doi.org/10.17341/gazimmfd.1253193
AMA Asan ME, Taşkın H, Alemdar M, Capoglu R. Tiroit kanseri hastalık tanısında lojistik regresyon kullanımı. GUMMFD. Mayıs 2024;39(3):1509-1524. doi:10.17341/gazimmfd.1253193
Chicago Asan, Mehmet Emin, Harun Taşkın, Murat Alemdar, ve Recayi Capoglu. “Tiroit Kanseri hastalık tanısında Lojistik Regresyon kullanımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39, sy. 3 (Mayıs 2024): 1509-24. https://doi.org/10.17341/gazimmfd.1253193.
EndNote Asan ME, Taşkın H, Alemdar M, Capoglu R (01 Mayıs 2024) Tiroit kanseri hastalık tanısında lojistik regresyon kullanımı. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39 3 1509–1524.
IEEE M. E. Asan, H. Taşkın, M. Alemdar, ve R. Capoglu, “Tiroit kanseri hastalık tanısında lojistik regresyon kullanımı”, GUMMFD, c. 39, sy. 3, ss. 1509–1524, 2024, doi: 10.17341/gazimmfd.1253193.
ISNAD Asan, Mehmet Emin vd. “Tiroit Kanseri hastalık tanısında Lojistik Regresyon kullanımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 39/3 (Mayıs 2024), 1509-1524. https://doi.org/10.17341/gazimmfd.1253193.
JAMA Asan ME, Taşkın H, Alemdar M, Capoglu R. Tiroit kanseri hastalık tanısında lojistik regresyon kullanımı. GUMMFD. 2024;39:1509–1524.
MLA Asan, Mehmet Emin vd. “Tiroit Kanseri hastalık tanısında Lojistik Regresyon kullanımı”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 39, sy. 3, 2024, ss. 1509-24, doi:10.17341/gazimmfd.1253193.
Vancouver Asan ME, Taşkın H, Alemdar M, Capoglu R. Tiroit kanseri hastalık tanısında lojistik regresyon kullanımı. GUMMFD. 2024;39(3):1509-24.