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Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning

Author

Listed:
  • Mukesh Kumar

    (School of Computer Application, Lovely Professional University, Phagwara 144402, India)

  • Saurabh Singhal

    (Department of Computer Engineering and Applications, GLA University, Mathura 281406, India)

  • Shashi Shekhar

    (Department of Computer Engineering and Applications, GLA University, Mathura 281406, India)

  • Bhisham Sharma

    (Department of Computer Science & Engineering, Chitkara University School of Engineering and Technology, Chitkara University, Baddi 174103, India)

  • Gautam Srivastava

    (Department of Mathematics and Computer Science, Brandon University, Brandon, MB R7A 6A9, Canada
    Research Centre for Interneural Computing, China Medical University, Taichung 40402, Taiwan
    Department of Computer Science and Math, Lebanese American University, Beirut 1102, Lebanon)

Abstract

Breast cancer is the most frequently encountered medical hazard for women in their forties, affecting one in every eight women. It is the greatest cause of death worldwide, and early detection and diagnosis of the disease are extremely challenging. Breast cancer currently exceeds all other female cancers, including ovarian cancer. Researchers can use access to healthcare records to find previously unknown healthcare trends. According to the National Cancer Institute (NCI), breast cancer mortality rates can be lowered if the disease is detected early. The novelty of our work is to develop an optimized stacking ensemble learning (OSEL) model capable of early breast cancer prediction. A dataset from the University of California, Irvine repository was used, and comparisons to modern classifier models were undertaken. The implementation analyses reveal the unique approach’s efficacy and superiority when compared to existing contemporary categorization models (AdaBoostM1, gradient boosting, stochastic gradient boosting, CatBoost, and XGBoost). In every classification task, predictive models may be used to predict the class level, and the current research explores a range of predictive models. It is better to integrate multiple classification algorithms to generate a set of prediction models capable of predicting each class level with 91–99% accuracy. On the breast cancer Wisconsin dataset, the suggested OSEL model attained a maximum accuracy of 99.45%, much higher than any single classifier. Thus, the study helps healthcare professionals find breast cancer and prevent it from happening.

Suggested Citation

  • Mukesh Kumar & Saurabh Singhal & Shashi Shekhar & Bhisham Sharma & Gautam Srivastava, 2022. "Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning," Sustainability, MDPI, vol. 14(21), pages 1-26, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13998-:d:955166
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    References listed on IDEAS

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    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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    Cited by:

    1. Melwin D. Souza & G. Ananth Prabhu & Varuna Kumara & K. M. Chaithra, 2024. "EarlyNet: a novel transfer learning approach with VGG11 and EfficientNet for early-stage breast cancer detection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 4018-4031, August.
    2. Hao Wang & Chen Peng & Bolin Liao & Xinwei Cao & Shuai Li, 2023. "Wind Power Forecasting Based on WaveNet and Multitask Learning," Sustainability, MDPI, vol. 15(14), pages 1-22, July.

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