Affiliation:
1. Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
Abstract
Accurate diagnosis of breast cancer using automated algorithms continues to be a challenge in the literature. Although researchers have conducted a great deal of work to address this issue, no definitive answer has yet been discovered. This challenge is aggravated further by the fact that most available datasets have imbalanced class issues, meaning that the number of cases in one class vastly outnumbers those of the others. The goal of this study was to (i) develop a reliable machine-learning-based prediction model for breast cancer based on the combination of the resampling technique and the classifier, which we called a ‘fusion model’; (ii) deal with a typical high-class imbalance problem, which is posed because the breast cancer patients’ class is significantly smaller than the healthy class; and (iii) interpret the model output to understand the decision-making mechanism. In a comparative analysis with three well-known classifiers representing classical learning, ensemble learning, and deep learning, the effectiveness of the proposed machine-learning-based approach was investigated in terms of metrics related to both generalization capability and prediction accuracy. Based on the comparative analysis, the fusion model (random oversampling techniques dataset + extreme gradient boosting classifier) affects the accuracy, precision, recall, and F1-score with the highest value of 99.9%. On the other hand, for ROC evaluation, the oversampling and hybrid sampling techniques dataset combined with extreme gradient boosting achieved 100% performance compared to the models combined with the undersampling techniques dataset. Thus, the proposed predictive model based on the fusion strategy can optimize the performance of breast cancer diagnosis classification.
Funder
Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference58 articles.
1. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries;Sung;CA Cancer J. Clin.,2021
2. Collaborative modeling of the benefits and harms associated with different U.S. Breast cancer screening strategies;Mandelblatt;Ann. Intern. Med.,2016
3. Radiologists’ Performance and Their Enjoyment of Interpreting Screening Mammograms;Geller;AJR Am. J. Roentgenol.,2009
4. Epidemiology of cancer in Saudi Arabia thru 2010–2019: A systematic review with constrained meta-analysis;Alqahtani;AIMS Public Health,2020
5. Breast cancer prediction and detection using data mining classification algorithms: A comparative study;Tehnicki Vjesnik,2019
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Enhancing Prediction Models' Performance for Breast Cancer using SMOTE Technique;2023 3rd International Conference on Emerging Smart Technologies and Applications (eSmarTA);2023-10-10
2. A Comparison of Oversampling and Undersampling Methods in Sentiment Analysis Regarding Indonesia Fuel Price Increase Using Support Vector Machine;2023 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS);2023-08-02