Universiti Teknologi Malaysia Institutional Repository

Comparison on some machine learning techniques in breast cancer classification

Mashudi, N. A. and Rossli, S. A. and Ahmad, N. and Mohd. Noor, N. (2021) Comparison on some machine learning techniques in breast cancer classification. In: 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020, 1 - 3 March 2021, Virtual, Langkawi Island.


Official URL: http://dx.doi.org/10.1109/IECBES48179.2021.9398837


Breast cancer is the second most common cancer after lung cancer and one of the main causes of death worldwide. Women have a higher risk of breast cancer as compared to men. Thus, one of the early diagnosis with an accurate and reliable system is critical in breast cancer treatment. Machine learning techniques are well known and popular among researchers, especially for classification and prediction. An investigation was conducted to evaluate the performance of breast cancer classification for malignant tumors and benign tumors using various machine learning techniques, namely k-Nearest Neighbors (k-NN), Random Forest, and Support Vector Machine (SVM) and ensemble techniques to compute the prediction of the breast cancer survival by implementing 10-fold cross validation. Additionally, the proposed methods are classified using 2-fold, 3-fold, and 5-fold cross validation to meet the best accuracy rate. This study used a dataset obtained from Wisconsin Diagnostic Breast Cancer (WDBC) with 23 selected attributes measured from 569 patients, from which 212 patients have malignant tumors and 357 patients have benign tumors. The performance evaluation of the proposed methods was computed to obtain accuracy, sensitivity, and specificity. Comparison results between all methods show that AdaBoost ensemble methods gave the highest accuracy at 98.77% for 10-fold cross validation, while 2-fold and 3-fold cross validation at 98.41% and 98.24%, respectively. Nevertheless, the result with 5-fold cross validation show SVM produced the best accuracy rate at 98.60% with the lowest error rate.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:breast cancer, classification, machine learning
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:95683
Deposited By: Narimah Nawil
Deposited On:31 May 2022 21:04
Last Modified:31 May 2022 21:04

Repository Staff Only: item control page