Empirical evaluation of classifiers for breast cancer diagnosis

Hassan Masoud Darya; Ali Bou Nassif; Mohammad AlShabi

doi:10.1117/12.2632637

8 June 2022 Empirical evaluation of classifiers for breast cancer diagnosis

Hassan Masoud Darya, Ali Bou Nassif, Mohammad AlShabi

Proceedings Volume 12123, Smart Biomedical and Physiological Sensor Technology XIX; 121230D (2022) https://doi.org/10.1117/12.2632637
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Breast cancer is the second most type of cancer diagnosed in women; it is also the leading cause of cancer caused deaths in women after lung cancer. Breast lumps can be classified as cancerous and non-cancerous. Non-cancerous breast lump development is very common in women. It is important to correctly diagnose the type of breast lump to administer the correct treatment and give the needed care and attention. Intensive research is being done to improve the diagnosis of the type of breast lumps. In this paper we will study different machine learning algorithms for the diagnosis of breast tumors and to predict whether its cancerous or non-cancerous. In this paper we will be building four different classification methods SVM, KNN, RF and CART. We will be using the breast cancer Wisconsin (diagnostic) dataset to train the models. We will base the performance of our models based on the accuracy and other classification evaluation parameters. For the final model we were able to achieve a prediction model with an f1 score of 0.9927.

Citation Download Citation

Hassan Masoud Darya, Ali Bou Nassif, and Mohammad AlShabi "Empirical evaluation of classifiers for breast cancer diagnosis", Proc. SPIE 12123, Smart Biomedical and Physiological Sensor Technology XIX, 121230D (8 June 2022); https://doi.org/10.1117/12.2632637

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KEYWORDS

Breast

Breast cancer

Data modeling

Tumors

Machine learning

Cancer

Diagnostics

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