Breast Cancer Classification Using z-score Thresholding and Machine Learning

Mustafa Eren Yildirim; Yucel B. Salman

doi:10.35882/jeeemi.v7i4.1165

Mustafa Eren Yildirim Electronics and Communications Engineering, American University of Malta, Bormla, Malta; Department of Electrical & Electronics Engineering, Bahcesehir University, Istanbul, Turkey https://orcid.org/0000-0002-0662-2770
Yucel B. Salman Department of Software Engineering, Bahcesehir University, Istanbul, Turkey https://orcid.org/0000-0001-5038-1612

DOI: https://doi.org/10.35882/jeeemi.v7i4.1165

Keywords: breast cancer, outlier detection, z-score, wdbc

Abstract

Image processing and machine learning are being used in biomedical purposes as a supporting tool in the detection and diagnosis of certain diseases. Breast cancer is one of these diseases which the researchers have put great effort into for decades. To accomplish this task, image and feature-based public datasets are available to be used. Due to several reasons such as hardware or preprocessing, images can get noisy. The noise in images which can lead to anormal / outliers in the dataset may decrease the detection accuracy and can mislead the medical staff during diagnosis stage. Therefore, this study aims to present the effect of removing the outliers from dataset on the detection accuracy of breast cancer. The method removes the outliers detected by z-score analysis. The remaining data is normalized, and classification accuracy of 10 methods are obtained by direct implementation. The methods are XGBoost, Neural Network, CNN, RNN, AdaBoost, LSTM, GRU, Random Forest, SVM and Logistic Regression. A public dataset Wisconsin diagnosis breast cancer (WDBC) was used in this study. Ablation study was conducted by fine-tuning the threshold value of z-score method. The result showed that the best accuracy was obtained when the threshold value is 3. Also, comparison was made between the results made on the entire dataset and dataset after its outliers were removed. The results showed that the average accuracy of all the classifiers is 98.08%. As a conclusion, the results indicate that removal of the outliers from the dataset increases the overall accuracy of breast cancer detection.

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