Komparasi Algoritma Machine Learning dalam Klasifikasi Kanker Payudara

 Nurfadlan Afiatuddin (STMIK Amik Riau, Pekanbaru, Indonesia)
 M Teguh Wicaksono (STMIK Amik Riau, Pekanbaru, Indonesia)
 Vitto Rezky Akbar (STMIK Amik Riau, Pekanbaru, Indonesia)
 (*)Rahmaddeni Rahmaddeni Mail (STMIK Amik Riau, Pekanbaru, Indonesia)
 Denok Wulandari (Institut Az Zuhra, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: February 2, 2024; Published: April 30, 2024

Abstract

Every year, millions of women are faced with a serious global health issue: breast cancer. This research aims to improve the efficiency of breast cancer classification using machine learning. One of the main challenges encountered is the imbalance between the number of malignant and benign cases in the dataset. Therefore, this study aims to compare the performance of several machine learning algorithms in classifying breast cancer, such as Decision Tree, Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Random Forest. Preprocessing data, dividing data with various ratios, and testing various classification algorithms are the techniques used in this research. The dataset used originates from the Wisconsin Breast Cancer Diagnosis dataset from the Kaggle platform. The Synthetic Minority Over-Sampling Technique (SMOTE) is used to achieve balance in the proportions of imbalanced classes. After hyperparameter tuning, Logistic Regression showed the best performance with accuracy reaching 100% in several situations. This study concludes that the use of machine learning, especially with techniques for handling class imbalance, can improve the ability to detect breast cancer early. Additionally, this research also helps understand the best algorithms to improve accuracy in classifying breast cancer, providing support for healthcare professionals in early diagnosis, and enhancing the quality of patient care.

Keywords


Breast Cancer; Machine Learning; Class Imbalance; SMOTE; Classification Accuracy

Full Text:

PDF


Article Metrics

Abstract view : 383 times
PDF - 159 times

References

Organisasi Kesehatan Dunia (World Health Organization), “Breast cancer.” [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/breast-cancer

M. Arnold et al., “Current and future burden of breast cancer: Global statistics for 2020 and 2040,” Breast, vol. 66, no. September, pp. 15–23, 2022, doi: 10.1016/j.breast.2022.08.010.

W. KOUWENAAR, “On cancer incidence in Indonesia.,” Acta Unio Int. Contra Cancrum, vol. 7, no. 1 Spec. No., pp. 61–71, 1951, [Online]. Available: https://gco.iarc.fr/today/data/factsheets/populations/360-indonesia-fact-sheets.pdf

M. Kepala Biro Komunikasi dan Pelayanan Masyarakat drg. Widyawati, “Kanker Payudara Paling Banyak di Indonesia, Kemenkes Targetkan Pemerataan Layanan Kesehatan.” [Online]. Available: https://sehatnegeriku.kemkes.go.id/baca/umum/20220202/1639254/kanker-payudaya-paling-banyak-di-indonesia-kemenkes-targetkan-pemerataan-layanan-kesehatan/

S. Ara, A. Das, and A. Dey, “Malignant and Benign Breast Cancer Classification using Machine Learning Algorithms,” 2021 Int. Conf. Artif. Intell. ICAI 2021, pp. 97–101, 2021, doi: 10.1109/ICAI52203.2021.9445249.

S. R. Gupta, “Prediction time of breast cancer tumor recurrence using Machine Learning,” Cancer Treat. Res. Commun., vol. 32, no. July, p. 100602, 2022, doi: 10.1016/j.ctarc.2022.100602.

“American Cancer Society Recommendations for the Early Detection of Breast Cancer,” 2023, [Online]. Available: https://www.cancer.org/cancer/types/breast-cancer/screening-tests-and-early-detection/american-cancer-society-recommendations-for-the-early-detection-of-breast-cancer.html

National Breast Cancer Foundation, “Breast Self-Exam,” 2024, [Online]. Available: https://www.nationalbreastcancer.org/breast-self-exam/

M. Javaid, A. Haleem, R. Pratap Singh, R. Suman, and S. Rab, “Significance of machine learning in healthcare: Features, pillars and applications,” Int. J. Intell. Networks, vol. 3, no. May, pp. 58–73, 2022, doi: 10.1016/j.ijin.2022.05.002.

M. Nurkholifah, Jasmarizal, Y. Umar, and Rahmaddeni, “Analisa Performa Algoritma Machine Learning Dalam Prediksi Penyakit Liver,” J. Indones. Manaj. Inform. dan Komun., vol. 4, no. 1, pp. 164–172, 2023, doi: 10.35870/jimik.v4i1.149.

K. M. M. Uddin, N. Biswas, S. T. Rikta, and S. K. Dey, “Machine learning-based diagnosis of breast cancer utilizing feature optimization technique,” Comput. Methods Programs Biomed. Updat., vol. 3, no. February, p. 100098, 2023, doi: 10.1016/j.cmpbup.2023.100098.

K. Kousalya, B. Krishnakumar, C. I. Shanthosh, R. Sharmila, and V. Sneha, “Diagnosis of breast cancer using machine learning algorithms,” Int. J. Adv. Sci. Technol., vol. 29, no. 3 Special Issue, pp. 970–974, 2020.

M. M. Hassan et al., “A comparative assessment of machine learning algorithms with the Least Absolute Shrinkage and Selection Operator for breast cancer detection and prediction,” Decis. Anal. J., vol. 7, no. April, p. 100245, 2023, doi: 10.1016/j.dajour.2023.100245.

V. Birchha and B. Nigam, “Performance Analysis of Averaged Perceptron Machine Learning Classifier for Breast Cancer Detection,” Procedia Comput. Sci., vol. 218, no. 2022, pp. 2181–2190, 2022, doi: 10.1016/j.procs.2023.01.194.

Y. Hendra Kusuma, S. Supraapto, and Y. Setiawan, “Analisis Kepuasan Penumpang pada Maskapai Penerbangan Menggunakan Algoritma C4.5 dan Naïve Bayes,” SENTIMAS Semin. Nas. Penelit. dan Pengabdi. Masy., pp. 162–171, 2022, [Online]. Available: https://journal.irpi.or.id/index.php/sentimas/article/view/320/125

R. Gonzalez, P. Nejat, A. Saha, C. J. V. Campbell, A. P. Norgan, and C. Lokker, “Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review,” J. Pathol. Inform., vol. 15, no. August 2023, p. 100348, 2024, doi: 10.1016/j.jpi.2023.100348.

P. Gupta and S. Garg, “Breast Cancer Prediction using varying Parameters of Machine Learning Models,” Procedia Comput. Sci., vol. 171, pp. 593–601, 2020, doi: 10.1016/j.procs.2020.04.064.

E. Gentili et al., “Machine learning from real data: A mental health registry case study,” Comput. Methods Programs Biomed. Updat., vol. 5, no. August 2023, p. 100132, 2023, doi: 10.1016/j.cmpbup.2023.100132.

A. N. Kasanah, M. Muladi, and U. Pujianto, “Penerapan Teknik SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Objektivitas Berita Online Menggunakan Algoritma KNN,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 196–201, 2019, doi: 10.29207/resti.v3i2.945.

R. Resmiati and T. Arifin, “Klasifikasi Pasien Kanker Payudara Menggunakan Metode Support Vector Machine dengan Backward Elimination,” Sistemasi, vol. 10, no. 2, p. 381, 2021, doi: 10.32520/stmsi.v10i2.1238.

J. KUSUMA, B. H. HAYADI, W. WANAYUMINI, and R. ROSNELLY, “Komparasi Metode Multi Layer Perceptron (MLP) dan Support Vector Machine (SVM) untuk Klasifikasi Kanker Payudara,” MIND J., vol. 7, no. 1, pp. 51–60, 2022, doi: 10.26760/mindjournal.v7i1.51-60.

H. Harafani and H. A. Al-Kautsar, “Meningkatkan Kinerja K-Nn Untuk Klasifikasi Kanker Payudara Dengan Forward Selection,” J. Pendidik. Teknol. dan Kejuru., vol. 18, no. 1, p. 99, 2021, doi: 10.23887/jptk-undiksha.v18i1.29905.

A. I. S. Azis, Irma Surya Kumala Idris, Budy Santoso, and Yasin Aril Mustofa, “Pendekatan Machine Learning yang Efisien untuk Prediksi Kanker Payudara,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 3, pp. 458–469, 2019, doi: 10.29207/resti.v3i3.1347.

M. A. Naji, S. El Filali, K. Aarika, E. H. Benlahmar, R. A. Abdelouhahid, and O. Debauche, “Machine Learning Algorithms for Breast Cancer Prediction and Diagnosis,” Procedia Comput. Sci., vol. 191, pp. 487–492, 2021, doi: 10.1016/j.procs.2021.07.062.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Komparasi Algoritma Machine Learning dalam Klasifikasi Kanker Payudara

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.



JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.