{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T14:34:40Z","timestamp":1790606080442,"version":"4.1.0"},"reference-count":72,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"project \u201cNet4SCIENCE: Applied doctoral and postdoctoral research network in the fields of smart specialization Health and Bioeconomy\u201d","award":["POCU\/993\/6\/13\/154722"],"award-info":[{"award-number":["POCU\/993\/6\/13\/154722"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Identifying skeletal remains has been and will remain a challenge for forensic experts and forensic anthropologists, especially in disasters with multiple victims or skeletal remains in an advanced stage of decomposition. This study examined the performance of two machine learning (ML) algorithms in predicting the person\u2019s sex based only on the morphometry of L1\u2013L5 lumbar vertebrae collected recently from Romanian individuals. The purpose of the present study was to assess whether by using the machine learning (ML) techniques one can obtain a reliable prediction of sex in forensic identification based only on the parameters obtained from the metric analysis of the lumbar spine. Method: This paper built and tuned predictive models with two of the most popular techniques for classification, RF (random forest) and XGB (xgboost). Both series of models used cross-validation and a grid search to find the best combination of hyper-parameters. The best models were selected based on the ROC_AUC (area under curve) metric. Results: The L1\u2013L5 lumbar vertebrae exhibit sexual dimorphism and can be used as predictors in sex prediction. Out of the eight significant predictors for sex, six were found to be particularly important for the RF model, while only three were determined to be important by the XGB model. Conclusions: Even if the data set was small (149 observations), both RF and XGB techniques reliably predicted a person\u2019s sex based only on the L1\u2013L5 measurements. This can prove valuable, especially when only skeletal remains are available. With minor adjustments, the presented ML setup can be transformed into an interactive web service, freely accessible to forensic anthropologists, in which, after entering the L1\u2013L5 measurements of a body\/cadaver, they can predict the person\u2019s sex.<\/jats:p>","DOI":"10.3390\/diagnostics13243630","type":"journal-article","created":{"date-parts":[[2023,12,11]],"date-time":"2023-12-11T11:24:43Z","timestamp":1702293883000},"page":"3630","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning Models for Prediction of Sex Based on Lumbar Vertebral Morphometry"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2435-6058","authenticated-orcid":false,"given":"Madalina Maria","family":"Diac","sequence":"first","affiliation":[{"name":"Forensic Medicine Sciences Department, Institute of Legal Medicine, University of Medicine and Pharmacy \u201cGrigore T. Popa\u201d, 700115 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gina Madalina","family":"Toma","sequence":"additional","affiliation":[{"name":"Forensic Medicine Department, \u201cSf. Ioan\u201d Hospital Suceava, University of Medicine and Pharmacy \u201cGrigore T. Popa\u201d, 700115 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simona Irina","family":"Damian","sequence":"additional","affiliation":[{"name":"Forensic Medicine Sciences Department, Institute of Legal Medicine, University of Medicine and Pharmacy \u201cGrigore T. Popa\u201d, 700115 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marin","family":"Fotache","sequence":"additional","affiliation":[{"name":"Alexandru Ioan Cuza University, 700506 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolae","family":"Romanov","sequence":"additional","affiliation":[{"name":"Alexandru Ioan Cuza University, 700506 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1516-6981","authenticated-orcid":false,"given":"Daniel","family":"Tabian","sequence":"additional","affiliation":[{"name":"Department of Fundamental, Prophylactic and Clinical Disciplines, Medicine Faculty, Transilvania University of Brasov, 500019 Brasov, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriela","family":"Sechel","sequence":"additional","affiliation":[{"name":"Department of Fundamental, Prophylactic and Clinical Disciplines, Medicine Faculty, Transilvania University of Brasov, 500019 Brasov, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei","family":"Scripcaru","sequence":"additional","affiliation":[{"name":"Forensic Medicine Sciences Department, University of Medicine and Pharmacy \u201cGrigore T. Popa\u201d, 700115 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monica","family":"Hancianu","sequence":"additional","affiliation":[{"name":"Pharmacy Department, University of Medicine and Pharmacy \u201cGrigore T. Popa\u201d, 700115 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diana Bulgaru","family":"Iliescu","sequence":"additional","affiliation":[{"name":"Forensic Medicine Sciences Department, Institute of Legal Medicine, University of Medicine and Pharmacy \u201cGrigore T. 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