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Using Machine Learning Algorithms for Breast Cancer Diagnosis

Author

Listed:
  • Mazen Mobtasem El-Lamey

    (Computer Engineering Department, Nile University, Egypt)

  • Mohab Mohammed Eid

    (Computer Engineering Department, Nile University, Egypt)

  • Muhammad Gamal

    (Computer Engineering, Nile University, Egypt)

  • Nour-Elhoda Mohamed Bishady

    (Mechanical Engineering, Nile University, Egypt)

  • Ali Wagdy Mohamed

    (Cairo University, Nile University, Egypt)

Abstract

There are many cancer patients, especially breast cancer patients as it is the most common type of cancer. Due to the huge number of breast cancer patients, many breast cancer-focused hospitals aren't able to process the huge number of patients and might expose some women to late stages of cancer. Thus, the automation of the process can help these hospitals in speeding up the process of cancer detection. In this paper, the authors test several machine learning models such as k-nearest neighbours (KNN), support vector machine (SVM), and artificial neural network (ANN). They then compare their accuracies and losses with themselves and other models that have been developed by other researchers to see whether their approach is efficient or not and to decide what machine learning algorithm is best to use.

Suggested Citation

  • Mazen Mobtasem El-Lamey & Mohab Mohammed Eid & Muhammad Gamal & Nour-Elhoda Mohamed Bishady & Ali Wagdy Mohamed, 2021. "Using Machine Learning Algorithms for Breast Cancer Diagnosis," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 12(4), pages 117-137, October.
    • Handle: RePEc:igg:jamc00:v:12:y:2021:i:4:p:117-137
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