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Research Article Open access CC BY 4.0

Can the Physical Parameters with the Support Vector Machine (SVM) Method Able to Classify Benign and Malignant Breast Cancer?

Anak Agung Ngurah Gunawan, I. Made Satriya Wibawa, Anak Agung Ngurah Surya Mahendra, Anak Agung Ngurah Franky Kusuma Negara, Anak Agung Ngurah Frady Cakra Negara

Asian Journal of Research in Computer Science · pp. 8–20 · Published 30 Sep 2022

10.9734/ajrcos/2022/v14i4288

Abstract

Objective: Evaluating the diagnostic performance of SVM to classify benign and malignant by performing a meta-analysis. Methods: The data used for this study were secondary data. It consisted of 221 mammogram images (mean age 57.5 years) with 164 malignant and 57 benign, taken from a radiological database that has been examined by a radiologist with more than 20 years of experience. Also, histopathological record data that had been examined by an oncologist with more than 20 years of experience. Mammograms were taken from January 2022 to June 2022. In all, 221 mammograms consisting of 164 malignant and 57 benign were used as SVM method training, and 20 mammograms consisting of 10 malignant and 10 benign were used to test the performance of the SVM method. It was then evaluated using pathology results as the gold standard. Results: Benign had a significantly lower deviation (an average of 29.2661230 ± 10.14916673) than malignant (an average of 33.1841234 ± 11.70238757). The SVM method performance value obtained the values ​​of TP, FP, TN, FN, accuracy, sensitivity, Specificity, and Precision, respectively 7,7, 3, 3, 50%, 70%, 30%, and 50%. Conclusion: A proper performance to distinguish benign and malignant can be obtained using the physical deviation parameters with the SVM classification approach. However, these findings should be proven in larger datasets with different mammographic scanners. Our meta-analysis shows that the physical parameters and SVM have high sensitivity but low specificity. Of the nine physical parameters in the mammogram, only the parameter deviation was significant to distinguish between benign and malignant. The SVM method proved to be able to differentiate between benign and malignant.

Mammography benign malignant SVM physical parameters

References (5)

  1. 1 Time to enhancement derived from ultrafast breast MRI as a novel parameter to discriminate benign from malignant breast lesions [DOI]
  2. 2 The diagnostic value of sonoelastographic strain ratio in discriminating malignant from benign solid breast masses [DOI]
  3. 3 The added value of qualitative and quantitative diffusion-weighted magnetic resonance imaging (DW-MRI) in differentiating benign from malignant breast lesions [DOI]
  4. 4 Role of supersonic shear wave imaging quantitative elastography (SSI) in differentiating benign and malignant solid breast masses [DOI]
  5. 5 Classification of breast cancer grades using physical parameters and K-nearest neighbor method [DOI]

Cited by 2

Investigating the Role of Feature Variation and Data Transformations of Different Types of Machine Learning Algorithms in Classifying Benign - Malignant Breast Cancer

Anak Agung Ngurah Gunawana, Putu Astri Novianti, Anak Agung Ngurah Frady Cakra Negara · 2025

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