Data mining for sex estimation based on cranial measurements

• Published in: Forensic science international Vol. 315; p. 110441
• Main Authors: Toneva, Diana H., Nikolova, Silviya Y., Agre, Gennady P., Zlatareva, Dora K., Hadjidekov, Vassil G., Lazarov, Nikolai E.
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.10.2020; Elsevier Limited
• Subjects: Accuracy; Adolescent; Adult; adults; Age; Aged; Aged, 80 and over; Algorithms; Anatomic Landmarks; Bones; Cephalometry; Classification; Classification rules; Computed tomography; Cranial measurements; data collection; Data mining; Data Mining - methods; Data processing; Datasets; decision support systems; Decision Trees; Digitization; Exports; Forensic anthropology; Forensic Anthropology - methods; Forensic anthropology population data; Forensic sciences; Gender differences; Humans; Imaging, Three-Dimensional; Learning algorithms; Logistic Models; Machine Learning; Methods; Middle Aged; Regression analysis; Regression models; Rule induction; Sex; Sex Determination by Skeleton - methods; Sex estimation; Sexes; Skull; Skull - anatomy & histology; Skull - diagnostic imaging; Software; Support vector machines; Tomography, X-Ray Computed; Young Adult; Classification rules; Sex estimation; Decision trees; Machine learning; Forensic anthropology population data; Cranial measurements
• ISSN: 0379-0738; 1872-6283; 1872-6283
• DOI: 10.1016/j.forsciint.2020.110441

•Machine learning algorithms are useful sex classifiers.•Classification rules and decision trees provide high accuracy rates.•The best sex classification accuracy is 91.9 %.•Nonstandard cranial measurements can be used for sex estimation. The aim of the present study is to develop effective and understandable classification models for sex estimation and to identify the most dimorphic linear measurements in adult crania by means of data mining techniques. Furthermore, machine learning models and models developed through logistic regression analysis are compared in terms of performance. Computed tomography scans of 393 adult individuals were used in the study. A landmark-based approach was applied to collect the metric data. The three-dimensional coordinates of 47 landmarks were acquired and used for calculation of linear measurements. Two datasets of cranial measurements were assembled, including 37standard measurements and 1081 interlandmark distances, respectively. Three data mining algorithms were applied: the rule induction algorithms JRIP and Ridor, and the decision tree algorithm J48. Two advanced attribute selection methods (Weka BestFirst and Weka GeneticSearch) were also used. The best accuracy result (91.9 %) was achieved by a set of rules learnt by the JRIP algorithm from the dataset constructed by application of the GeneticSearch selection algorithm to the dataset of standard cranial measurements. The set consisted of five rules including seven cranial measurements. Its accuracy was even better than the classification rates achieved by the logistic regression models. Concerning the second dataset of nonstandard measurements, the best accuracy (88.3 %) was obtained by using classification models learnt by two algorithms – JRIP with a dataset preprocessed by the BestFirst selection algorithm and Ridor with preprocessing by the GeneticSearch selection algorithm. Our experiments show that for the two datasets mentioned above the rule-based models contain smaller sets of rules with shorter lists of measurements and achieve better classification accuracy results in comparison with decision tree-based models.