The potential of synthetic minority oversampling technique to enhance the precision of gender prediction: an investigation of artificial neural networks with cephalometry

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Abstract

BACKGROUND: When creating models utilizing artificial neural networks, the quantity of training data and the distribution of data need to be considered, particularly when making gender predictions.

AIM: This study seeks to determine the potential impact of using the synthetic minority oversampling technique (SMOTE) on gender prediction using the artificial neural networks model.

MATERIALS AND METHODS: The current study utilized a dataset consisting of 297 cephalometric measurements from Indonesian patients, comprising 229 samples from females and 68 samples from males. WebCeph is used to measure certain parameters, such as Sella-Nation-Point A (SNA) angle, mandibular length, mandibular angle, Sella-Glabella-Point A (SGA) angle, and diagnosis. Data processing and artificial neural networks model creation were conducted using Python.

RESULTS: The gender identification accuracy of the artificial neural networks model is 87% for females and 0% for males, resulting in an overall average accuracy of 78%. When using SMOTE, the accuracy is 22%, with 0% for females and 37% for males. However, when using SMOTE and normalization, the accuracy increases to 71%, with 82% for females and 30% for males. The accuracy of normalization without SMOTE is 76%, with 86% for females and 14% for males.

CONCLUSIONS: This research has proven the efficacy of SMOTE in improving the classification of male matrices. Nevertheless, this study reveals that the overall accuracy results of SMOTE are suboptimal in comparison to the absence of SMOTE and normalization. The application of data balancing strategies is necessary to achieve optimal accuracy in gender prediction when artificial neural networks, and other parameters must be applied.

About the authors

Vitria Wuri Handayani

Universitas Airlangga; Pontianak Polytechnic Health Ministry

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

MD, Medical Faculty; Nursing Department

Indonesia, Surabaya; Pontianak

Ahmad Yudianto

Universitas Airlangga

Author for correspondence.
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

Indonesia, 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

Indonesia, 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

Indonesia, Surabaya

Muhammad Rasyad Caesarardhi

Institut Teknologi Sepuluh Nopember

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

MD, Department of Information Systems

Indonesia, 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

Indonesia, Surabaya

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Supplementary files

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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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