Data mining for sex estimation based on cranial measurements.
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 regre...
| Publicado en: | Forensic Science International Vol. 315 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Elsevier B.V.
Oct2020
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145699154&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145699154 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03790738 3L0 jtl: Forensic Science International issn: 03790738 maglogo: N pubinfo: dt: Oct2020 vid: 315 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 145699154 145699154 NLM32781389 10.1016/j.forsciint.2020.110441 NLM32781389 145699154 ppct: 1 formats: tig: atl: Data mining for sex estimation based on cranial measurements. aug: au: Toneva, Diana H. Nikolova, Silviya Y. Agre, Gennady P. Zlatareva, Dora K. Hadjidekov, Vassil G. Lazarov, Nikolai E. affil: Department of Anthropology and Anatomy, Institute of Experimental Morphology, Pathology and Anthropology with Museum, Bulgarian Academy of Sciences, 1113, Sofia, Bulgaria sug: subj: Data Mining Methods Algorithms Diagnosis, Laboratory Methods Tomography, X-Ray Computed Skull Aged, 80 and Over Aged Decision Trees Middle Age Skull Anatomy and Histology Body Regions Forensic Anthropology Adult Adolescence Cephalometry Logistic Regression Young Adult Imaging, Three-Dimensional Aged, 80 & over Aged: 65+ years Middle Aged: 45-64 years Adult: 19-44 years Adolescent: 13-18 years ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|