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AuthorBidmos, Mubarak A.
AuthorOlateju, Oladiran I.
AuthorLatiff, Sabiha
AuthorRahman, Tawsifur
AuthorChowdhury, Muhammad E.H.
Available date2022-12-25T06:13:28Z
Publication Date2022-01-01
Publication NameInternational Journal of Legal Medicine
Identifierhttp://dx.doi.org/10.1007/s00414-022-02899-7
CitationBidmos, M.A., Olateju, O.I., Latiff, S. et al. Machine learning and discriminant function analysis in the formulation of generic models for sex prediction using patella measurements. Int J Legal Med (2022). https://doi.org/10.1007/s00414-022-02899-7
ISSN09379827
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85139468018&origin=inward
URIhttp://hdl.handle.net/10576/37553
AbstractSex prediction from bone measurements that display sexual dimorphism is one of the most important aspects of forensic anthropology. Some bones like the skull and pelvis display distinct morphological traits that are based on shape. These morphological traits which are sexually dimorphic across different population groups have been shown to provide an acceptably high degree of accuracy in the prediction of sex. A sample of 100 patella of Mixed Ancestry South Africans (MASA) was collected from the Dart collection. Six parameters: maximum height (maxh), maximum breadth (maxw), maximum thickness (maxt), the height of articular facet (haf), lateral articular facet breadth (lafb), and medial articular facet breath (mafb) were used in this study. Stepwise and direct discriminant function analyses were performed for measurements that exhibited significant differences between male and female mean measurements, and the “leave-one-out” approach was used for validation. Moreover, we have used eight classical machine learning techniques along with feature ranking techniques to identify the best feature combinations for sex prediction. A stacking machine learning technique was trained and validated to classify the sex of the subject. Here, we have used the top performing three ML classifiers as base learners and the predictions of these models were used as inputs to different machine learning classifiers as meta learners to make the final decision. The measurements of the patella of South Africans are sexually dimorphic and this observation is consistent with previous studies on the patella of different countries. The range of average accuracies obtained for pooled multivariate discriminant function equations is 81.9–84.2%, while the stacking ML technique provides 90.8% accuracy which compares well with those presented for previous studies in other parts of the world. In conclusion, the models proposed in this study from measurements of the patella of different population groups in South Africa are useful resent with reasonably high average accuracies.
SponsorOpen Access funding provided by the Qatar National Library.
Languageen
PublisherSpringer Nature
SubjectDiscriminant function analyses
Forensic anthropology
Machine learning
Patella
Sex prediction
TitleMachine learning and discriminant function analysis in the formulation of generic models for sex prediction using patella measurements
TypeArticle
ESSN1437-1596
dc.accessType Full Text


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