MDBT görüntülerinden elde edilen patellar ölçümler ile bir makine öğrenme algoritması olan karar ağacı kullanılarak cinsiyet tahmini
Abstract
Amaç: Bu çalışmanın amacı, patellanın multi-detektör bilgisayarlı tomografi (MDCT) görüntüleri üzerinden yapılan ölçümlerinden, bir makine öğrenmesi olan karar ağacı (DT) metodu kullanılarak cinsiyet belirlenmesi yapılıp yapılamayacağını ortaya koymaktır.
Materyal ve Metot: Çalışmaya 219 erkek ve 131 kadın bireye ait MDCT görüntüleri dahil edilmiştir. Radyolog tarafından, ortogonal düzleme getirilen patellanın anteroposterior (Ap), kraniokaudal (Kk), transvers (Trv) uzunlukları ölçüm aracıyla ölçülmüş ve volümleri (Vol) hesaplanmıştır. Patellar uzunluk ölçümlerine öncelikle lineer ayırt edici aykırı değer tespiti yapılmış, bu sayede cinsiyeti tahmin için veriler temizlenmiştir. DT için performans kriteri olarak karışıklık matrisi üzerinden hesaplanan Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), F1-Score (F1) ve Matthew’s Correlation Coefficient (Mcc) ölçütleri kullanılmıştır.
Bulgular: Erkeklerin Ap, Trv, Kk ve Vol değerleri kadınlardan daha yüksek olarak bulunmuş ve aralarında anlamlı fark tespit edilmiştir (pAp, Trv, Kk, Vol=0.000). Bu ölçümler ile erkek bireyleri tahmin etme oranı %98,2, kadın bireyleri tahmin etme oranı %98,4 olarak hesaplanmıştır.
Sonuç: Patella morfometrisine dayalı DT analizi, cinsiyet tahmini için basit, yeterli ve oldukça doğru bir yaklaşım sağlamıştır. Ayrıca araştırmacılara herhangi bir bilgisayara ihtiyaç duymadan sadece ağaç yapısı üzerinde dallanmaları ve cut-off değerleri kullanarak cinsiyet tahmini yapmak konusunda avantaj sağlamaktadır.
Keywords
karar ağacı cinsiyet tahmini lineer diskriminant analizi patella multi-dedektör bilgisayarlı tomografi osteometri
Supporting Institution
yok
Project Number
yok
Thanks
yok
References
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- 2. Akhlaghi M, Sheikhazadi A, Naghsh A, Dorvashi G. Identification of sex in Iranian population using patella dimensions. J Forensic Leg Med. 2010;17(3):150-5.
- 3. Abdel Moneim WM, Abdel Hady RH, Abdel Maaboud RM, Fathy HM, Hamed AM. Identification of sex depending on radiological examination of foot and patella. Am J Forensic Med Pathol. 2008;29(2):136-40.
- 4. Grewal DS, Khangura RK, Sircar K, Tyagi KK, Kaur G, David S. Morphometric Analysis of Odontometric Parameters for Gender Determination. J Clin Diagn Res. 2017;11(8):ZC09-ZC13.
- 5. Darmawan MF, Yusuf SM, Kadir MR, Haron H. Comparison on three classification techniques for sex estimation from the bone length of Asian children below 19 years old: an analysis using different group of ages. Forensic Sci Int. 2015;247:130 e1-11.
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- 31. du Jardin P, Ponsaille J, Alunni-Perret V, Quatrehomme G. A comparison between neural network and other metric methods to determine sex from the upper femur in a modern French population. Forensic Sci Int. 2009;192(1-3):127 e1-6.
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- 33. Curate F, Albuquerque A, Ferreira I, Cunha E. Sex estimation with the total area of the proximal femur: A densitometric approach. Forensic Sci Int. 2017;275:110-6.
- 34. Kruger GC, L'Abbe EN, Stull KE. Sex estimation from the long bones of modern South Africans. Int J Legal Med. 2017;131(1):275-85.
- 35. Nguyen HV, Gopalkrishnan V, editors. Feature extraction for outlier detection in high-dimensional spaces. Feature Selection in Data Mining; 2010.
Estimation of Gender by Using Decision Tree, a Machine Learning Algorithm, With Patellar Measurements Obtained From MDCT Images
Abstract
Aim: The present study aimed to analyze whether gender could be determined with the decision tree (DT) method, a machine learning algorithm, based on patellar multidetector computed tomography (MDCT) image measurements.
Material and Methods: The study was conducted on 219 male and 131 female MDCT images. The patellar anteroposterior (Ap), craniocaudal (Cc), transverse (Trv) length and volume (Vol), adjusted on the orthogonal plane by the radiologist, were calculated. In patellar length measurements, initially linear discriminant outliers were detected to clear the data for gender prediction. Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), F1-Score (F1) and Matthew’s Correlation Coefficient (Mcc) criteria were taken as the performance criteria for DT.
Results: It was determined that male Ap, Trv, Cc, and Vol values were higher when compared to the female values and there was a significant difference between these values based on gender (pAp, Trv, Cc, Vol = 0.000). Using the above-mentioned measurements, it was calculated that the prediction rate for male individuals was 98.2% and for female individuals, it was 98.4%.
Conclusion: DT analysis based on patella morphometry provided a simple, adequate and highly accurate approach for gender estimation. Furthermore, it was determined that it would provide an advantage for researchers in gender prediction using only branching and cut-off values on the tree structure without the need to use a computer.
Keywords
decision tree gender estimation linear discriminant analysis multidetector computed tomography osteometry
Project Number
yok
References
- 1. Peckmann TR, Meek S, Dilkie N, Rozendaal A. Determination of sex from the patella in a contemporary Spanish population. J Forensic Leg Med. 2016;44:84-91.
- 2. Akhlaghi M, Sheikhazadi A, Naghsh A, Dorvashi G. Identification of sex in Iranian population using patella dimensions. J Forensic Leg Med. 2010;17(3):150-5.
- 3. Abdel Moneim WM, Abdel Hady RH, Abdel Maaboud RM, Fathy HM, Hamed AM. Identification of sex depending on radiological examination of foot and patella. Am J Forensic Med Pathol. 2008;29(2):136-40.
- 4. Grewal DS, Khangura RK, Sircar K, Tyagi KK, Kaur G, David S. Morphometric Analysis of Odontometric Parameters for Gender Determination. J Clin Diagn Res. 2017;11(8):ZC09-ZC13.
- 5. Darmawan MF, Yusuf SM, Kadir MR, Haron H. Comparison on three classification techniques for sex estimation from the bone length of Asian children below 19 years old: an analysis using different group of ages. Forensic Sci Int. 2015;247:130 e1-11.
- 6. Iwamura ES, Soares-Vieira JA, Munoz DR. Human identification and analysis of DNA in bones. Rev Hosp Clin Fac Med Sao Paulo. 2004;59(6):383-8.
- 7. Jantz RL, Kimmerle EH, Baraybar JP. Sexing and stature estimation criteria for Balkan populations. J Forensic Sci. 2008;53(3):601-5.
- 8. Ross AH. Regional isolation in the Balkan region: an analysis of craniofacial variation. Am J Phys Anthropol. 2004;124(1):73-80.
- 9. Kemkes-Grottenthaler A. Sex determination by discriminant analysis: an evaluation of the reliability of patella measurements. Forensic Sci Int. 2005;147(2-3):129-33.
- 10. Peckmann TR, Fisher B. Sex estimation from the patella in an African American population. J Forensic Leg Med. 2018;54:1-7.
- 11. Savall F, Faruch-Bilfeld M, Dedouit F, Sans N, Rousseau H, Rouge D, et al. Metric Sex Determination of the Human Coxal Bone on a Virtual Sample using Decision Trees. J Forensic Sci. 2015;60(6):1395-400.
- 12. Song YY, Lu Y. Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry. 2015;27(2):130-5.
- 13. Welcome to Python.org [Internet]. 2019.
- 14. Seabold S, Perktold J, editors. Statsmodels: Econometric and statistical modeling with python. Proceedings of the 9th Python in Science Conference; 2010: Austin, TX.
- 15. McKinney W. pandas: a foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing. 2011;14(9).
- 16. Buitinck L, Louppe G, Blondel M, Pedregosa F, Mueller A, Grisel O, et al. API design for machine learning software: experiences from the scikit-learn project. arXiv preprint arXiv:13090238. 2013.
- 17. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learning in Python. the Journal of machine Learning research. 2011;12:2825-30.
- 18. Gomez M, De Benzo Z, Gomez C, Marcano E, Torres H, Ramirez M. Comparison of methods for outlier detection and their effects on the classification results for a particular data base. Analytica chimica acta. 1990;239:229-43.
- 19. Du P, Xiaoqing D, editors. The application of decision tree in gender classification. 2008 Congress on Image and Signal Processing; 2008: IEEE.
- 20. Gupta B, Rawat A, Jain A, Arora A, Dhami N. Analysis of various decision tree algorithms for classification in data mining. International Journal of Computer Applications. 2017;163(8):15-9.
- 21. Robinson MS, Bidmos MA. The skull and humerus in the determination of sex: reliability of discriminant function equations. Forensic Sci Int. 2009;186(1-3):86 e1-5.
- 22. Puisoru M, Forna N, Fatu AM, Fatu R, Fatu C. Analysis of mandibular variability in humans of different geographic areas. Ann Anat. 2006;188(6):547-54.
- 23. Saini V, Srivastava R, Rai RK, Shamal SN, Singh TB, Tripathi SK. Mandibular ramus: an indicator for sex in fragmentary mandible. J Forensic Sci. 2011;56 Suppl 1:S13-6.
- 24. Turan MK, Oner Z, Secgin Y, Oner S. A trial on artificial neural networks in predicting sex through bone length measurements on the first and fifth phalanges and metatarsals. Comput Biol Med. 2019;115:103490.
- 25. Bidmos MA, Steinberg N, Kuykendall KL. Patella measurements of South African whites as sex assessors. Homo. 2005;56(1):69-74.
- 26. Steyn M, Iscan MY. Osteometric variation in the humerus: sexual dimorphism in South Africans. Forensic Sci Int. 1999;106(2):77-85.
- 27. Dayal MR, Bidmos MA. Discriminating sex in South African blacks using patella dimensions. J Forensic Sci. 2005;50(6):1294-7.
- 28. Phoophalee P, Prasitwattanaseree S, Riengrojpitak S, Mahakkanukrauh P, editors. Sex determination by patella measurements in Thais. Proceedings of 1st Asean Plus Three Graduate Research Congress, Chiang Mai; 2012.
- 29. Sakaue K. New Method for Diagnosis of the Sex and Age-at-death of an Adult Human Skeleton from the Patella. Bull Natl Mus Nat Sci, Ser D2008. p. 43-51.
- 30. Introna FJ, Di Vella G, Campobasso CP. Sex determination by discriminant analysis of patella measurements. Forensic Sci Int. 1998;95(1):39-45.
- 31. du Jardin P, Ponsaille J, Alunni-Perret V, Quatrehomme G. A comparison between neural network and other metric methods to determine sex from the upper femur in a modern French population. Forensic Sci Int. 2009;192(1-3):127 e1-6.
- 32. Mahfouz M, Badawi A, Merkl B, Fatah EE, Pritchard E, Kesler K, et al. Patella sex determination by 3D statistical shape models and nonlinear classifiers. Forensic Sci Int. 2007;173(2-3):161-70.
- 33. Curate F, Albuquerque A, Ferreira I, Cunha E. Sex estimation with the total area of the proximal femur: A densitometric approach. Forensic Sci Int. 2017;275:110-6.
- 34. Kruger GC, L'Abbe EN, Stull KE. Sex estimation from the long bones of modern South Africans. Int J Legal Med. 2017;131(1):275-85.
- 35. Nguyen HV, Gopalkrishnan V, editors. Feature extraction for outlier detection in high-dimensional spaces. Feature Selection in Data Mining; 2010.
Details
| Primary Language | English |
|---|---|
| Subjects | Clinical Sciences |
| Journal Section | Original Articles |
| Authors | |
| Project Number | yok |
| Publication Date | January 23, 2021 |
| Acceptance Date | January 18, 2021 |
| Published in Issue | Year 2021 Volume: 3 Issue: 1 |
Cited By
Chief Editors
Assoc. Prof. Zülal Öner
Address: İzmir Bakırçay University, Department of Anatomy, İzmir, Türkiye
Assoc. Prof. Deniz Şenol
Address: Düzce University, Department of Anatomy, Düzce, Türkiye
E-mail: medrecsjournal@gmail.com
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Medical Records Association (Tıbbi Kayıtlar Derneği)
Address: Düzce / Türkiye
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