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All Journal Bulletin of Electrical Engineering and Informatics
Tamim Khan
Daffodil International University
Electrical & Electronics Engineering

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Breast cancer detection: an effective comparison of different machine learning algorithms on the Wisconsin dataset Md. Murad Hossin; F. M. Javed Mehedi Shamrat; Md Rifat Bhuiyan; Rabea Akter Hira; Tamim Khan; Shourav Molla
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4448

Abstract

According to the American cancer society, breast cancer is one of the leading causes of women's mortality worldwide. Early identification and treatment are the most effective approaches to halt the spread of this cancer. The objective of this article is to give a comparison of eight machine learning algorithms, including logistic regression (LR), random forest (RF), K-nearest neighbors (KNN), decision tree (DT), ada boost (AB), support vector machine (SVM), gradient boosting (GB), and Gaussian Naive Bayes (GNB) for breast cancer detection. The breast cancer Wisconsin (diagnostic) dataset is being utilized to validate the findings of this study. The comparison was made using the following performance metrics: accuracy, sensitivity, false omission rate, specificity, false discovery rate and area under curve. The LR method achieved a maximum accuracy of 99.12% among all eight algorithms and was compared to other comparable studies in the literature. The five features chosen are used to calculate the model's fidelity-to-interpretability ratio (FIR), which indicates how much interpretability was sacrificed for performance. The uniqueness of this work is the explainability approach taken in the model's performance, which aims to make the model's outputs more understandable and interpretable to healthcare experts.
Co-Authors F. M. Javed Mehedi Shamrat Md Rifat Bhuiyan Md. Murad Hossin Rabea Akter Hira Shourav Molla