Hybrid Sampling untuk Meningkatkan Akurasi Deteksi Kanker Serviks pada Data Tidak Seimbang: Kajian Komparatif
DOI:
https://doi.org/10.26905/jasiek.v7i2.16134Keywords:
Borderline-SMOTE, Cervical Cancer, Class Imbalance, Early Detection, ResamplingAbstract
Cervical Cancer has a high mortality rate among women, driving the adoption of early detection systems based on machine learning. However, their implementation is hindered by class imbalance issues, as seen in the UCI Cervical Cancer Behavior Risk Dataset, where positive cases constitute only 5.8–7.3% of the data. This study proposes an evaluation of resampling techniques—including SMOTE, ADASYN, Random Undersampling, and Borderline-SMOTE—combined with classification algorithms such as RF, XGBoost, LR, GNB, and k-NN. Using Stratified K-Fold Cross Validation to preserve the original class distribution in each fold and ensuring resampling is applied only to the training data in each iteration, the results demonstrate that Borderline-SMOTE significantly improved model performance. Specifically, the Random Forest model achieved a Recall of 0.87 and an AUC-ROC of 0.94. These findings are expected to provide a foundation for future research focused on optimizing adaptive sampling methods
Downloads
References
World Health Organization (WHO), “Cervical Cancer,” World Health Organization (WHO), Mar. 2024. https://www.who.int/news-room/fact-sheets/detail/Cervical-Cancer (accessed Aug. 01, 2025).
N. Al Mudawi and A. Alazeb, “A Model for Predicting Cervical Cancer Using Machine learning Algorithms,” Sensors, vol. 22, no. 11, p. 4132, May 2022, doi: 10.3390/s22114132.
M. L. R. University of California, Irvine, “Cervical Cancer (Risk Factors) Data Set,” UCI Repository, 2017.
V. López, A. Fernández, S. García, V. Palade, and F. Herrera, “An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics,” Inf. Sci. (Ny)., vol. 250, pp. 113–141, Nov. 2013, doi: 10.1016/j.ins.2013.07.007.
A. Fernández, S. García, M. Galar, R. C. Prati, B. Krawczyk, and F. Herrera, Learning from Imbalanced Data Sets. Cham: Springer International Publishing, 2018. doi: 10.1007/978-3-319-98074-4.
Y. Shi, Y. Wan, K. Wu, and X. Chen, “Non-negativity and locality constrained Laplacian sparse coding for image classification,” Expert Syst. Appl., vol. 72, pp. 121–129, Apr. 2017, doi: 10.1016/j.eswa.2016.12.012.
M. L. R. University of California, Irvine, “Cervical Cancer (Risk Factors) Data Set,” UCI Repository, 2017. https://archive.ics.uci.edu/ml/datasets/Cervical+Cancer+Behavior+Risk
V. López, A. Fernández, S. García, V. Palade, and F. Herrera, “An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics,” Inf. Sci. (Ny)., vol. 250, pp. 113–141, Nov. 2013, doi: 10.1016/j.ins.2013.07.007.
A. Fernández, S. García, M. Galar, R. C. Prati, B. Krawczyk, and F. Herrera, Learning from Imbalanced Data Sets. Cham: Springer International Publishing, 2018. doi: 10.1007/978-3-319-98074-4.
Y. Shi, Y. Wan, K. Wu, and X. Chen, “Non-negativity and locality constrained Laplacian sparse coding for image classification,” Expert Syst. Appl., vol. 72, pp. 121–129, Apr. 2017, doi: 10.1016/j.eswa.2016.12.012.
N. Almugren and H. Alshamlan, “A survey on hybrid feature selection methods in microarray gene expression data for Cancer classification,” IEEE Access, vol. 7, pp. 78533–78548, 2019, doi: 10.1109/ACCESS.2019.2922987.
M. Buda, A. Maki, and M. A. Mazurowski, “A systematic study of the class imbalance problem in convolutional neural networks,” Neural Networks, vol. 106, pp. 249–259, Oct. 2018, doi: 10.1016/j.neunet.2018.07.011.
D. Elreedy and A. F. Atiya, “A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for handling class imbalance,” Inf. Sci. (Ny)., vol. 505, pp. 32–64, Dec. 2019, doi: 10.1016/j.ins.2019.07.070.
G. Kovács, “An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets,” Appl. Soft Comput., vol. 83, p. 105662, Oct. 2019, doi: 10.1016/j.asoc.2019.105662.
H. Mo, H. Sun, J. Liu, and S. Wei, “Developing window behavior models for residential buildings using XGBoost algorithm,” Energy Build., vol. 205, pp. 1–23, 2019, doi: 10.1016/j.enbuild.2019.109564.
M. Z. Islam, V. Estivill-Castro, M. A. Rahman, and T. Bossomaier, “Combining K-MEANS and a genetic algorithm through a novel arrangement of genetic operators for high quality clustering,” Expert Syst. Appl., vol. 91, pp. 402–417, 2018, doi: 10.1016/j.eswa.2017.09.005.
A. Geron, Aurélien Géron - Hands on Machine learning with Scikit Learn Keras and TensorFlow. 2nd Edition-O’Reilly Media (2019), vol. 1, no. 0. O’Reilly Media, 2019. [Online]. Available: http://repo.iain-tulungagung.ac.id/5510/5/BAB 2.pdf
K. Fernandes, J. S. Cardoso, and J. Fernandes, “Transfer Learning with Partial Observability Applied to Cervical Cancer Screening,” 2017, pp. 243–250. doi: 10.1007/978-3-319-58838-4_27.
A. Javed, B. S. Lee, and D. M. Rizzo, “A benchmark study on time series clustering,” arXiv, vol. 1, no. June, p. 100001, 2020, doi: 10.1016/j.mlwa.2020.100001.
L. Breiman, Springer Series in Statistics The Elements of. CRC Press, 2017.
T. Chen, “XGBoost : A Scalable Tree Boosting System”.
C. Bishop, Information Science and Statistics. Springer International Publishing, 2006.
K. Murphy, Machine learning: A Probabilistic Perspective, 1st ed. The MIT Press, 2012.
R. Duda, P. Hart, and D. Stork, Patern Classification, 2nd ed. New York: Wiley, 2012.
J. N. Mandrekar, “Receiver Operating Characteristic Curve in Diagnostic Test Assessment,” J. Thorac. Oncol., vol. 5, no. 9, pp. 1315–1316, Sep. 2010, doi: 10.1097/JTO.0b013e3181ec173d.
Ş. K. Çorbacıoğlu and G. Aksel, “Receiver operating characteristic curve analysis in diagnostic accuracy studies,” Turkish J. Emerg. Med., vol. 23, no. 4, pp. 195–198, Oct. 2023, doi: 10.4103/tjem.tjem_182_23.
[S. A. Hicks et al., “On evaluation metrics for medical applications of artificial intelligence,” Sci. Rep., vol. 12, no. 1, p. 5979, Apr. 2022, doi: 10.1038/s41598-022-09954-8.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
The journal allow the authors to hold the copyright without restrictions and allow the authors to retain publishing rights without restrictions.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
