A Machine Learning Model for Determination of Gender Utilizing Hybrid Classifiers


  • Dewi Nasien Institut Bisnis dan Teknologi Pelita Indonesia
  • M. Hasmil Adiya Institut Bisnis dan Teknologi Pelita Indonesia
  • Yusnita Rahayu Universitas Riau
  • Dahliyusmanto Dahliyusmanto Universitas Riau
  • Erlin Erlin Institut Bisnis dan Teknologi Pelita Indonesia
  • Devi Willieam Anggara Universiti Teknologi Malaysia




ANN-SVM, PCA, Pelvic, Femur, Gender_Determination


One part of forensic anthropology involves investigating skeletal remains to identify corpses, and many of these remains were found incomplete, burned, broken, or destroyed, making investigation challenging. This study aims to use the pelvis and femur to identify the gender of skeletal remains. The pelvis and femur have previously been proven to be accurate indicators of a corpse's gender. The identification process is done through the measurement of the subpubic angle of the pelvis and the angle taken straight down from the top of the femur to the patella and then straight up. The two measurements were combined using the principal component analysis (PCA) method into two attributes on the x and y axes. These attributes were later used as data for the machine learning model design. The design process consisted of an Artificial Neutral Network (ANN) design model and Support Vector Machine (SVM) design model combined into a hybrid machine learning system. The ANN and SVM hybrid machine learning were tested with acquired data. The result of the test using the confusion matrix showed 83.33% accuracy, which is categorized as "good classification" based on Area Under the Curve (AUC).


Download data is not yet available.


Afrianty, I., Nasien, D., & Haron, H. (2022). Performance Analysis of Support Vector Machine in Sex Classification of The Sacrum Bone in Forensic Anthropology. JURNAL TEKNIK INFORMATIKA, 15(1), 63–72. https://doi.org/10.15408/JTI.V15I1.25254

Afrianty, I., Nasien, D., Kadir, M. R. A., Haron, H., Azar, A. T., & Vaidyanathan, S. (2015). Back-Propagation neural network for gender determination in forensic anthropology. Studies in Computational Intelligence, 575, 255–281. https://doi.org/10.1007/978-3-319-11017-2_11

Akhlaghi, M., Azizian, A., Sadeghian, M. H., Azizian, F., Shahabi, Z., Rafiee, S., & Mousavi, F. (2019). Collo-Diaphyseal Angle as an Optimal Anthropometric Criterion of Femur in Gender Determination. International Journal of Medical Toxicology and Forensic Medicine, 9(2), 65–74. https://doi.org/10.32598/ijmtfm.v9i2.24986

Al-Boeridi, O. N., Syed Ahmad, S. M., & Koh, S. P. (2015). A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: study case on Malay word recognition. Neural Computing and Applications, 26, 1505-1513.

Alfsdotter, C. (2021). Forensic archaeology and forensic anthropology within Swedish law enforcement: current state and suggestions for future developments. Forensic Science International: Reports, 3. https://doi.org/10.1016/j.fsir.2021.100178

Anil K, J., & Jianchang, M. (1996). Artificial Neural Networks: A Tutorial. http://csc.lsu.edu/~jianhua/nn.pdf

Belousov, A. I., Verzakov, S. A., & Von Frese, J. (2002). Applicational aspects of support vector machines. Journal of Chemometrics, 16(8–10), 482–489. https://doi.org/10.1002/CEM.744

Benz, P., Zhang, C., Karjauv, A., & Kweon, I. S. (2021). Robustness may be at odds with fairness: An empirical study on class-wise accuracy. NeurIPS 2020 Workshop on Pre-Registration in Machine Learning, 325–342.

Beretta, L., & Santaniello, A. (2016). Nearest neighbor imputation algorithms: A critical evaluation. BMC Medical Informatics and Decision Making, 16(3), 197–208. https://doi.org/10.1186/S12911-016-0318-Z/TABLES/5

Cattaneo, C. (2007). Forensic anthropology: developments of a classical discipline in the new millennium. Forensic Science International, 165(2–3), 185–193. https://doi.org/10.1016/J.FORSCIINT.2006.05.018

Christensen, A. M., Leslie, W. D., & Baim, S. (2014). Ancestral differences in femoral neck axis length: possible implications for forensic anthropological analyses. Forensic Science International, 236, 193.e1-193.e4. https://doi.org/10.1016/J.FORSCIINT.2013.12.027

Christensen, A. M., Passalacqua, N. V., Bartelink, E. J., Christensen, A. M., Passalacqua, N. V., & Bartelink, E. J. (2014). Forensic Anthropology: Current Methods and Practice. In Forensic Anthropology (1st Edition). Academic Press.

Curate, F., Coelho, J., Gonçalves, D., Coelho, C., Ferreira, M. T., Navega, D., & Cunha, E. (2016). A method for sex estimation using the proximal femur. Forensic Science International, 266, 579.e1-579.e7. https://doi.org/10.1016/J.FORSCIINT.2016.06.011

Darmawan, M. F. Bin, Osman, M. Z., & Nasien, D. (2021). Sex Estimation Model for Asian based on Random Forest Using Length of Left-Hand Bone. 2021 2nd International Conference on Artificial Intelligence and Data Sciences, AiDAS 2021. https://doi.org/10.1109/AIDAS53897.2021.9574387

Dogan, N., & Tanrikulu, Z. (2013). A comparative analysis of classification algorithms in data mining for accuracy, speed and robustness. Information Technology and Management, 14(2), 105–124. https://doi.org/10.1007/s10799-012-0135-8

Erlin, Marlim, Y. N., Junadhi, Suryati, L., & Agustina, N. (2022a). Early Detection of Diabetes Using Machine Learning with Logistic Regression Algorithm. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 11(2), 88–96.

Erlin, Marlim, Y. N., Junadhi, Suryati, L., & Agustina, N. (2022b). Deteksi Dini Penyakit Diabetes Menggunakan Machine Learning dengan Algoritma Logistic Regression. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 11(2), 88–96. https://doi.org/10.22146/JNTETI.V11I2.3586

Fan, A. Z., & Koutris, P. (2022). Certifiable robustness for nearest neighbor classifiers. ArXiv Preprint ArXiv:2201.04770

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). Knowledge Discovery and Data Mining: Towards a Unifying Framework. KDD.

Franchini, G., Ruggiero, V., Porta, F., Zanni, L., Franchini, G., Ruggiero, V., Porta, F., & Zanni, L. (2023). Neural architecture search via standard machine learning methodologies. Mathematics in Engineering 2023 1:1, 5(1), 1–21. https://doi.org/10.3934/MINE.2023012

Gama, I., Navega, D., & Cunha, E. (2015). Sex estimation using the second cervical vertebra: a morphometric analysis in a documented Portuguese skeletal sample. International Journal of Legal Medicine, 129(2), 365–372. https://doi.org/10.1007/S00414-014-1083-0

Gorodetsky, V., & Samoylov, V. (2008). Feature Extraction for Machine Learning?: Logic – Probabilistic Approach. The Fourth Workshop on Feature Selection in Data Mining, 55–65.

Gorunescu, F. (2011). Data mining: Concepts, models and techniques. Intelligent Systems Reference Library, 12. https://doi.org/10.1007/978-3-642-19721-5/COVER

Hairuddin, N. L., Yusuf, L. M., Othman, M. S., & Nasien, D. (2021). Gender classification using a pso-based feature selection and optimised bpnn in forensic anthropology. International Journal of Computer Aided Engineering and Technology, 15(2–3), 232–242. https://doi.org/10.1504/IJCAET.2021.117133

Holland, T. D. (1991). Sex assessment using the proximal tibia. American Journal of Physical Anthropology, 85(2), 221–227. https://doi.org/10.1002/AJPA.1330850210

Korkmaz Tan, R., & Bora, ?. (2017). Parameter tuning algorithms in modeling and simulation. International Journal of Engineering Science and Application, 1(2), 58–66.

Kranioti, E. F., Bastir, M., Sánchez-Meseguer, A., & Rosas, A. (2009). A geometric-morphometric study of the Cretan humerus for sex identification. Forensic Science International, 189(1–3), 111.e1-111.e8. https://doi.org/10.1016/J.FORSCIINT.2009.04.013

Lian, J., Freeman, L., Hong, Y., & Deng, X. (2021). Robustness with respect to class imbalance in artificial intelligence classification algorithms. Journal of Quality Technology, 53(5), 505–525.

Lee, S., Lee, S., Lim, J., & Lee, S. (2013). Defect Diagnostics of Power Plant Gas Turbine Using Hybrid SVM-ANN Method. ASME 2012 Gas Turbine India Conference, GTINDIA 2012, 725–732. https://doi.org/10.1115/GTINDIA2012-9564

Macaluso, P. J., & Lucena, J. (2014a). Estimation of sex from sternal dimensions derived from chest plate radiographs in contemporary Spaniards. International Journal of Legal Medicine, 128(2), 389–395. https://doi.org/10.1007/S00414-013-0910-Z/METRICS

Macaluso, P. J., & Lucena, J. (2014b). Estimation of sex from sternal dimensions derived from chest plate radiographs in contemporary Spaniards. International Journal of Legal Medicine, 128(2), 389–395. https://doi.org/10.1007/S00414-013-0910-Z/METRICS

Mays, S. (1992). Taphonomic factors in a human skeletal assemblage. Circaea, 9(2), 54–58.

Meeusen, R. A., Christensen, A. M., & Hefner, J. T. (2015). The Use of Femoral Neck Axis Length to Estimate Sex and Ancestry. Journal of Forensic Sciences, 60(5), 1300–1304. https://doi.org/10.1111/1556-4029.12820

Mohamad, M., Selamat, A., Krejcar, O., Fujita, H., & Wu, T. (2020). An analysis on new hybrid parameter selection model performance over big data set. Knowledge-Based Systems, 192, 105441. https://doi.org/10.1016/j.knosys.2019.105441

Nasien, D., Adiya, M. H., Afrianty, I., Ali, N. A., Samah, A. A., & Rahayu, Y. (2021, September). Determination of Sex and Race in Forensic Anthropology: A Comparison of Artificial Neural Network and Support Vector Machine. In 2021 4th International Conference of Computer and Informatics Engineering (IC2IE) (pp. 51-55). IEEE.

Navega, D., Vicente, R., Vieira, D. N., Ross, A. H., & Cunha, E. (2015). Sex estimation from the tarsal bones in a Portuguese sample: a machine learning approach. International Journal of Legal Medicine, 129(3), 651–659. https://doi.org/10.1007/S00414-014-1070-5

Papaioannou, V. A., Kranioti, E. F., Joveneaux, P., Nathena, D., & Michalodimitrakis, M. (2012). Sexual dimorphism of the scapula and the clavicle in a contemporary Greek population: applications in forensic identification. Forensic Science International, 217(1–3), 231.e1-231.e7. https://doi.org/10.1016/J.FORSCIINT.2011.11.010

Paya, B. A., Esat, I. I., & Badi, M. N. M. (1997). Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mechanical Systems and Signal Processing, 11(5), 751–765. https://doi.org/10.1006/MSSP.1997.0090

Phenice, T. W. (1969). A newly developed visual method of sexing the os pubis. American Journal of Physical Anthropology, 30(2), 297–301. https://doi.org/10.1002/AJPA.1330300214

Rabunal, J. R., & Dorado, J. (2005). Artificial Neural Networks in Applications. In SciencesNew York. IGI Global. http://www.amazon.com/dp/1591409020

Santosh, K. C., Pradeep, N., Chakrabarti, T., Chakrabarti, P., Elngar, A. A., Nami, M., ... & Akbar, M. A. (2022). Performance Evaluation of LIBSVM and MSVM in Human Age Estimation and Gender Identification from Digital Images of Femur bone.

Shamim, N., & Yogesh. (2021). Machine Learning Based Feature Extraction of an Image: A Review. Proceedings of International Conference on Machine Intelligence and Data Science Applications: MIDAS 2020, 369–383.

Shylaja, S., & Muralidharan, R. (2019). Hybrid SVM-ANN Classifier is used for Heart Disease Prediction System. INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH, 07(3). www.ijedr.org

Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524. https://doi.org/10.1016/j.asoc.2019.105524

Spradley, M. K., & Jantz, R. L. (2011). Sex Estimation in Forensic Anthropology: Skull Versus Postcranial Elements. Journal of Forensic Sciences, 56(2), 289–296. https://doi.org/10.1111/J.155

Stewart, T. D. (Thomas D. (1979). Essentials of forensic anthropology, especially as developed in the United States. Thomas.

Stojanowski, C. M. , & S. R. M. (1999). A reevaluation of the sex prediction accuracy of the minimum supero-inferior femoral neck diameter for modern individuals. Journal of Forensic Sciences, 44(6), 1215–1218.

Suarez-Alvarez, M. M., Pham, D. T., Prostov, M. Y., & Prostov, Y.I. (2012). Statistical approach to normalization of feature vectors and clustering of mixed datasets. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 468(2145), 2630–2651. https://doi.org/10.1098/rspa.2011.0704

Thomas, R. M., Parks, C. L., & Richard, A. H. (2017). Accuracy Rates of Ancestry Estimation by Forensic Anthropologists Using Identified Forensic Cases. Journal of Forensic Sciences, 62(4), 971–974. https://doi.org/10.1111/1556-4029.13361

White, T. D., Black, M. T., Folkens, P. A., White, T. D., Black, M. T., & Folkens, P. A. (2012). Human Osteology. In Human Osteology. Academic Press. http://www.sciencedirect.com:5070/book/9780123741349/human-osteology

Ya?ar I?can, M., & Olivera, H. E. S. (2000). Forensic anthropology in Latin America. Forensic Science International, 109(1), 15–30. https://doi.org/10.1016/S0379-0738(99)00213-3




How to Cite

Nasien, D., Adiya, M. H., Rahayu, Y., Dahliyusmanto, D., Erlin, E., & Anggara, D. W. (2023). A Machine Learning Model for Determination of Gender Utilizing Hybrid Classifiers . Journal of Applied Engineering and Technological Science (JAETS), 5(1), 542–556. https://doi.org/10.37385/jaets.v5i1.1839