MDBT görüntülerinden elde edilen patellar ölçümler ile bir makine öğrenme algoritması olan karar ağacı kullanılarak cinsiyet tahmini

Year 2021, Volume: 3 Issue: 1, 1 - 9, 23.01.2021

Serkan Öner , Muhammed Turan , Zülal Öner

https://doi.org/10.37990/medr.843451

Cited By: 5

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

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Thanks

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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

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