首页|University of Derby Reports Findings in Machine Learning (A statistical evaluati on of the sexual dimorphism of the acetabulum in an Iberian population)
University of Derby Reports Findings in Machine Learning (A statistical evaluati on of the sexual dimorphism of the acetabulum in an Iberian population)
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New research on Machine Learning is th e subject of a report. According to news originating from Derby, United Kingdom, by NewsRx correspondents, research stated, "Sex estimation is essential for hum an identification within bioarchaeological and medico-legal contexts. Amongst th e sexually dimorphic skeletal elements commonly utilised for this purpose, the p elvis is usually preferred because of its direct relationship with reproduction. " Financial support for this research came from Universitat de Girona. Our news journalists obtained a quote from the research from the University of D erby, "Furthermore, the posterior part of the innominate bone has proven to have better preservation within degraded contexts. With the aim of investigating the potential of the vertical acetabular diameter as a sex marker, 668 documented i ndividuals from three different Iberian skeletal collections were randomly divid ed into training and test samples and eventually analysed using different statis tical approaches. Two traditional (Discriminant Function Analysis and Logistic R egression Analysis) and four Machine learning methodologies (Support Vector Clas sification, Decision Tree Classification, k Nearest Neighbour Classification, an d Neural Networks) were performed and compared. Amongst these statistical modali ties, Machine Learning methodologies yielded better accuracy outcomes, with DTC garnering highest accuracy percentages of 83.59% and 89.85% with the sex-pooled and female samples, respectively. With males, ANN yielded hi ghest accuracy percentage of 87.70%, when compared to other statist ical approaches. Higher accuracy obtained with ML, along with its minimal statis tical assumptions, warrant these approaches to be increasingly utilised for furt her investigations involving sex estimation and human identification. In this li ne, the creation of a statistical platform with easier user interface can render such robust statistical modalities accessible to researchers and practitioners, effectively maximising its practical use."
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