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Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Author

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  • Abdur Rasool

    (University of Chinese Academy of Sciences, Beijing 101408, China
    Shenzhen Key Lab for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    These authors contributed equally to this work.)

  • Chayut Bunterngchit

    (University of Chinese Academy of Sciences, Beijing 101408, China
    State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    These authors contributed equally to this work.)

  • Luo Tiejian

    (University of Chinese Academy of Sciences, Beijing 101408, China)

  • Md. Ruhul Islam

    (Department of Electrical Engineering and Computer Science, University of Stavanger, 4044 Stavanger, Norway)

  • Qiang Qu

    (Shenzhen Key Lab for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Qingshan Jiang

    (Shenzhen Key Lab for High Performance Data Mining, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

Abstract

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

Suggested Citation

  • Abdur Rasool & Chayut Bunterngchit & Luo Tiejian & Md. Ruhul Islam & Qiang Qu & Qingshan Jiang, 2022. "Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis," IJERPH, MDPI, vol. 19(6), pages 1-19, March.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3211-:d:767137
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    References listed on IDEAS

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    1. Diva Cristina Morett Romano Leão & Eliane Ramos Pereira & María Nieves Pérez-Marfil & Rose Mary Costa Rosa Andrade Silva & Angelo Braga Mendonça & Renata Carla Nencetti Pereira Rocha & María Paz Garcí, 2021. "The Importance of Spirituality for Women Facing Breast Cancer Diagnosis: A Qualitative Study," IJERPH, MDPI, vol. 18(12), pages 1-11, June.
    2. Eun Young Park & Myungsun Yi & Hye Sook Kim & Haejin Kim, 2021. "A Decision Tree Model for Breast Reconstruction of Women with Breast Cancer: A Mixed Method Approach," IJERPH, MDPI, vol. 18(7), pages 1-13, March.
    3. Wang, Haifeng & Zheng, Bichen & Yoon, Sang Won & Ko, Hoo Sang, 2018. "A support vector machine-based ensemble algorithm for breast cancer diagnosis," European Journal of Operational Research, Elsevier, vol. 267(2), pages 687-699.
    4. Kwang Ho Park & Erdenebileg Batbaatar & Yongjun Piao & Nipon Theera-Umpon & Keun Ho Ryu, 2021. "Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification," IJERPH, MDPI, vol. 18(4), pages 1-24, February.
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    Cited by:

    1. Tim Hulsen, 2022. "Data Science in Healthcare: COVID-19 and Beyond," IJERPH, MDPI, vol. 19(6), pages 1-4, March.

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