Keywords

forensic anthropology, sex estimation, decision trees, random forest modeling, machine learning

Abstract

Osteological sex estimation is a key component of the biological profile in forensic anthropological casework. However, there are still limitations with current methodologies for the skull as well as inadequate classification accuracies. Therefore, the purpose of this research is to improve osteological sex classification accuracies for the skull by combining morphological and metric variables into multiple models using decision trees and random forest (RF) modeling. The sample was derived from four U.S.-based skeletal collections and consisted of 403 individuals of European American and African American population affinities. Twenty-one morphological traits and 21 metric variables of the skull were selected for analysis, and intraobserver error was assessed to determine which variables should be incorporated into the models. Additionally, two-way ANOVAs and aligned rank transformation were utilized to examine the effects of sex, age, population affinity, and secular change on the variables. To generate the trees and RF models, 80% of the sample was used for model training and 20% of the sample was used for holdout validation testing. Multiple decision trees and RF models were generated that incorporated morphological, metric, and combined variables. Models were generated for the African American and European American samples, as well as for the pooled populations. The predictive accuracy of the models was assessed utilizing the holdout validation sample and the out-of-bag error. Overall, the majority of the combined data decision trees and RF models achieved higher classification accuracies compared to the separate morphological and metric models. Additionally, the pooled and European American models frequently achieved higher accuracies compared to the African American models. The combined data models also resulted in higher accuracies compared to popular osteological sex estimation methods for the skull. Therefore, the combined data models have great potential for use by forensic anthropologists and bioarchaeologists for estimating osteological sex from the skull.

Completion Date

2024

Semester

Summer

Committee Chair

John Schultz

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

Anthropology

Degree Program

Integrative Anthropological Sciences

Format

application/pdf

Identifier

DP0028569

URL

https://purls.library.ucf.edu/go/DP0028569

Language

English

Release Date

8-15-2025

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Campus-only Access)

Campus Location

Orlando (Main) Campus

Accessibility Status

Meets minimum standards for ETDs/HUTs

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Restricted to the UCF community until 8-15-2025; it will then be open access.

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