合成少数超采样技术提高性别鉴定准确性的潜力： 利用头颅测量学对人工神经网络的研究

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论证。在使用人工神经网络创建模型时，有必要考虑训练数据的数量及其分布，尤其是在预测性别时。

研究目的是确定合成少数超采样技术（synthetic minority oversampling technique, SMOTE）在使用人工神经网络确定死者性别方面的潜在有效性。

材料和方法。本研究使用的数据集包括对印度尼西亚患者（229 名女性和 68 名男性）进行的297次头颅测量。WebCeph 软件用于测量某些参数，如 SNA 角（Sella-Nation-Point A）、 下颌长度、下颌角、SGA 角（Sella-Glabella-Point A）和诊断。数据处理和人工神经网络的创建使用 Python 编程语言进行。

结果。使用人工神经网络进行性别鉴定的准确率为：女性 87%，男性 0%（平均 78%）。当使用 SMOTE 算法时，性别确定的准确率为 22%（女性为 0%，男性为 37%）。然而，当 SMOTE 算法与数据归一化结合使用时，准确率提高到 71%（女性为 82%，男性为 30%）。在不使用 SMOTE 算法的情况下，使用数据归一化的模型准确率为 76%（女性为 86%，男性为 14%）。

结论。这项研究证明了 SMOTE 在改进男性矩阵分类方面的有效性。然而，与不使用 SMOTE 和数据归一化的结果相比，总体准确度结果还不够理想。为了在使用人工神经网络和其他参数时实现性别确定的最佳精度，需要应用数据平衡策略。

作者简介

Vitria Wuri Handayani

Universitas Airlangga; Pontianak Polytechnic Health Ministry

Email: vitriawuri@gmail.com
ORCID iD: 0000-0002-5076-0118

MD, Medical Faculty; Nursing Department

印度尼西亚, Surabaya; Pontianak

Ahmad Yudianto

Universitas Airlangga

编辑信件的主要联系方式.
Email: ahmad-yudianto@fk.unair.ac.id
ORCID iD: 0000-0003-4754-768X

MD, PhD, Professor, Department of Forensics and Medicolegal, Faculty of Medicine; Magister of Forensic Sciences, Postgraduate School

印度尼西亚, Surabaya

MAR Mieke Sylvia

Univesitas Airlangga

Email: mieke-s-m-a-r@fkg.unair.ac.id
ORCID iD: 0000-0001-8821-0157

MD, PhD, Professor, Forensic Odontology Department, Dental Medical Faculty

印度尼西亚, Surabaya

Rulaningtyas Riries

Universitas Airlangga

Email: riries-r@fst.unair.ac.id
ORCID iD: 0000-0001-7058-1566

MD, Physics Department, Sains and Technology Faculty; Biomedical Department, Sains and Technology Faculty

印度尼西亚, Surabaya

Muhammad Rasyad Caesarardhi

Institut Teknologi Sepuluh Nopember

Email: mrasyadc@gmail.com
ORCID iD: 0000-0002-6986-0346

MD, Department of Information Systems

印度尼西亚, Surabaya

Ramadhan Putra

Universitas Airlangga

Email: ramadhan.hardani@fkg.unair.ac.id
ORCID iD: 0000-0002-0622-3892

MD, Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine

印度尼西亚, Surabaya

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2. Fig. 1. Analysis of cephalometric parameters using the front-end web application [13].

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3. Fig. 2. Graph of the success of the training and validation of ANN: a — training and validation accuracy without SMOTE and normalization; b — training and validation accuracy with SMOTE and without normalization; c — training and validation accuracy with SMOTE and normalization; d — training and validation accuracy with normalization and without SMOTE.

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4. Fig. 3. Graph of the failure of the training and validation of ANN: a — training and validation loss without SMOTE and normalization; b — training and validation loss with SMOTE and without normalization; c — training and validation loss with SMOTE and normalization; d — training and validation loss with normalization and without SMOTE.

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5. Fig. 4. SHapley Additive exPlanation values attribute to each feature the change in the expected model prediction when conditioning on that feature.

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