Digital Mammography Data Transformation Model to Improve the Accuracy of Benign and Malignant Detection in Breast Cancer

Authors

  • Anak Agung Ngurah Gunawan Department of Physics, Faculty of Mathematics and Natural Sciences, Udayana University, Bali, Indonesia.
  • I. Wayan Supardi Department of Physics, Faculty of Mathematics and Natural Sciences, Udayana University, Bali, Indonesia.
  • Nyoman Wendri Department of Physics, Faculty of Mathematics and Natural Sciences, Udayana University, Bali, Indonesia.
  • Anak Agung Ngurah Frady Cakra Negara Department of Informatics, Faculty of Mathematics and Natural Sciences, Udayana University, Bali Indonesia.
  • Anak Agung Ngurah Bagaskara Department of Physics, Faculty of Mathematics and Natural Sciences, Udayana University, Bali, Indonesia.
  • Putu Patriawan Radiology Department, Prof. Ngurah Hospital, Bali, Indonesia
  • I. Bagus Gede Dharmawan Radiology Department, Prima Medika Hospital, Bali, Indonesia

Keywords:

Benign, Breast cancer, KNN, Malignant, Mammography, Naïve Baye

Abstract

Digital mammography is a tool for early detection of breast cancer, but its sensitivity is low in dense breasts, and the false negatives are high. Therefore, this research aims to create a digital mammography data transformation model to improve the accuracy of benign and malignant detection in breast and compare it with other advanced methods. This research took data from Dokter Sutomo Hospital Surabaya and Sanglah Hospital Denpasar in the form of mammogram images taken from the hospital database with complete pathology results from 2010 to 2023. A total of 442 mammograms consisting of 114 benign and 328 malignant, 50% taken for training as many as 57 benign and 164 malignant, and 50% for trials as many as 57 benign and 164 malignant. This research used three methods: K-Nearest Neighbor (KNN), Naïve Bayes, and Support Vector Machine (SVM). Then, this research compared the three methods. Inclusion criteria: complete pathology results, no radiotherapy, and chemotherapy. The local hospital ethics committee approved this research. All patients were informed and obtained their verbal consent.The proposed KNN method with binary transformation had the best value with a sensitivity of 96.66% and False Negative 0%, compared to the Naïve Bayes method, which had a sensitivity value of 94.44% and False Negative 4%. In comparison, the SVM method had a sensitivity value of 84.85% and False Negative 4.16 %. Our meta-analysis showed that transformed physical parameters could increase sensitivity and decrease False Negatives. However, these findings must be proven on larger and multiple datasets with different mammography scanners

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I. Handayani, ‘IJISCS | 1 COMPARISON OF K-NEAREST NEIGHBOR AND NAÏVE BAYES FOR BREAST CANCER CLASSIFICATION USING PYTHON’.

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25.12.2023

How to Cite

Ngurah Gunawan, A. A. ., Supardi, I. W. ., Wendri, N. ., Ngurah Frady Cakra Negara, A. A. ., Ngurah Bagaskara, A. A. ., Patriawan, P. ., & Dharmawan, I. B. G. . (2023). Digital Mammography Data Transformation Model to Improve the Accuracy of Benign and Malignant Detection in Breast Cancer. International Journal of Intelligent Systems and Applications in Engineering, 12(1), 647–665. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4162

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Vol. 12 No. 1 (2024)

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