首页|Federal University of Uberlandia Reports Findings in Machine Learning (Explorato ry analysis of new craniometric measures for the investigation of biological sex using open-access statistical and machine-learning tools on a cone-beam compute d ...)
Federal University of Uberlandia Reports Findings in Machine Learning (Explorato ry analysis of new craniometric measures for the investigation of biological sex using open-access statistical and machine-learning tools on a cone-beam compute d ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Uberlandia, Brazil, by N ewsRx journalists, research stated, "Investigation of the biological sex of huma n remains is a crucial aspect of physical anthropology. However, due to varying states of skeletal preservation, multiple approaches and structures of interest need to be explored." The news correspondents obtained a quote from the research from the Federal Univ ersity of Uberlandia, "This research aims to investigate the potential use of di stances between bifrontal breadth (FMB), infraorbital foramina distance (IOD), n asal breadth (NLB), inter-canine width (ICD), and distance between mental forami na (MFD) for combined sex prediction through traditional statistical methods and through open-access machine-learning tools. Ethical approval was obtained from the ethics committee, and out of 100 cone beam computed tomography (CBCT) scans, 54 individuals were selected with all the points visible. Ten extra exams were chosen to test the predictors developed from the learning sample. Descriptive an alysis of measurements, standard deviation, and standard error were obtained. T- student and Mann- Whitney tests were utilized to assess the sex differences withi n the variables. A logistic regression equation was developed and tested for the investigation of the biological sex as well as decision trees, random forest, a nd artificial neural networks machine-learning models. The results indicate a st rong correlation between the measurements and the sex of individuals. When combi ned, the measurements were able to predict sex using a regression formula or mac hine learning based models which can be exported and added to software or webpag es. Considering the methods, the estimations showed an accuracy rate superior to 80% for males and 82% for females. All skulls in th e test sample were accurately predicted by both statistical and machine-learning models."
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