Random forest age estimation model based on length of left hand bone for Asian population
In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability a...
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Institute of Advanced Engineering and Science (IAES)
2020
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Online Access: | http://umpir.ump.edu.my/id/eprint/26627/1/Random%20forest%20age%20estimation%20model%20based%20on%20length.pdf http://umpir.ump.edu.my/id/eprint/26627/ http://doi.org/10.11591/ijece.v10i1.pp549-558 http://doi.org/10.11591/ijece.v10i1.pp549-558 |
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my.ump.umpir.266272020-02-26T07:56:29Z http://umpir.ump.edu.my/id/eprint/26627/ Random forest age estimation model based on length of left hand bone for Asian population Mohd Faaizie, Darmawan Ahmad Firdaus, Zainal Abidin Shahreen, Kasim Sutikno, Tole Budiarto, Rahmat GN Anthropology QA76 Computer software RA1001 Forensic Medicine. Medical jurisprudence. Legal medicine TK Electrical engineering. Electronics Nuclear engineering In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice. Institute of Advanced Engineering and Science (IAES) 2020-02 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/26627/1/Random%20forest%20age%20estimation%20model%20based%20on%20length.pdf Mohd Faaizie, Darmawan and Ahmad Firdaus, Zainal Abidin and Shahreen, Kasim and Sutikno, Tole and Budiarto, Rahmat (2020) Random forest age estimation model based on length of left hand bone for Asian population. International Journal of Electrical and Computer Engineering (IJECE), 10 (1). pp. 549-558. ISSN 2088-8708 http://doi.org/10.11591/ijece.v10i1.pp549-558 http://doi.org/10.11591/ijece.v10i1.pp549-558 |
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GN Anthropology QA76 Computer software RA1001 Forensic Medicine. Medical jurisprudence. Legal medicine TK Electrical engineering. Electronics Nuclear engineering Mohd Faaizie, Darmawan Ahmad Firdaus, Zainal Abidin Shahreen, Kasim Sutikno, Tole Budiarto, Rahmat Random forest age estimation model based on length of left hand bone for Asian population |
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In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice. |
format |
Article |
author |
Mohd Faaizie, Darmawan Ahmad Firdaus, Zainal Abidin Shahreen, Kasim Sutikno, Tole Budiarto, Rahmat |
author_facet |
Mohd Faaizie, Darmawan Ahmad Firdaus, Zainal Abidin Shahreen, Kasim Sutikno, Tole Budiarto, Rahmat |
author_sort |
Mohd Faaizie, Darmawan |
title |
Random forest age estimation model based on length of left hand bone for Asian population |
title_short |
Random forest age estimation model based on length of left hand bone for Asian population |
title_full |
Random forest age estimation model based on length of left hand bone for Asian population |
title_fullStr |
Random forest age estimation model based on length of left hand bone for Asian population |
title_full_unstemmed |
Random forest age estimation model based on length of left hand bone for Asian population |
title_sort |
random forest age estimation model based on length of left hand bone for asian population |
publisher |
Institute of Advanced Engineering and Science (IAES) |
publishDate |
2020 |
url |
http://umpir.ump.edu.my/id/eprint/26627/1/Random%20forest%20age%20estimation%20model%20based%20on%20length.pdf http://umpir.ump.edu.my/id/eprint/26627/ http://doi.org/10.11591/ijece.v10i1.pp549-558 http://doi.org/10.11591/ijece.v10i1.pp549-558 |
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