DeepBreast: Building Optimized Framework for Prognosis of Breast Cancer Classification Based on Computational Intelligence

Zeid, Magdy Abd-Elghany; El-Bahnasy, Khaled; Abo-Youssef, S. E.

doi:10.1109/miucc55081.2022.9781677

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Magdy et al [21] introduced an optimized framework for identifying breast cancer types and predicting breast cancer recurrence using seven ML algorithms: LR, XGboost, NB, RF, KNN, DT, and multilayer perception (MLP) of neural network. Grid search was employed to optimize the ML algorithms.The framework's performance was evaluated on the following Wisconsin datasets: the WBC dataset, the WDBC dataset, and the Wisconsin prognosis breast cancer (WPBC) dataset to determine the best-performing classifier.…”

Section: A Clinical Breast Cancer Datasetsmentioning

confidence: 99%

An Explainable Artificial Intelligence Model for the Classification of Breast Cancer

Khater,

Hussain,

Bendardaf

et al. 2024

IEEE Access

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Breast cancer is the most common cancer among women and globally affects both genders. The disease arises due to abnormal growth of tissue formed of malignant cells. Early detection of breast cancer is crucial for enhancing the survival rate. Therefore, artificial intelligence has revolutionized healthcare and can serve as a promising tool for early diagnosis. The present study aims to develop a machine-learning model to classify breast cancer and to provide explanations for the model results. This could improve the understanding of the diagnosis and treatment of breast cancer by identifying the most important features of breast cancer tumors and the way they affect the classification task. The bestperforming machine-learning model has achieved an accuracy of 97.7% using k-nearest neighbors and a precision of 98.2% based on the Wisconsin breast cancer dataset and an accuracy of 98.6% using the artificial neural network with 94.4% precision based on the Wisconsin diagnostic breast cancer dataset. Hence, this asserts the importance and effectiveness of the proposed approach. The present research explains the model behavior using model-agnostic methods, demonstrating that the bare nuclei feature in the Wisconsin breast cancer dataset and the area's worst feature Wisconsin diagnostic breast cancer dataset are the most important factors in determining breast cancer malignancy. The work provides extensive insights into the particular characteristics of the diagnosis of breast cancer and suggests possible directions for expected investigation in the future into the fundamental biological mechanisms that underlie the disease's onset. The findings underline the potential of machine learning to enhance breast cancer diagnosis and therapy planning while emphasizing the importance of interpretability and transparency in artificial intelligence-based healthcare systems.

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Section: A Clinical Breast Cancer Datasetsmentioning

confidence: 99%

An Explainable Artificial Intelligence Model for the Classification of Breast Cancer

Khater,

Hussain,

Bendardaf

et al. 2024

IEEE Access

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Accuracy Assessment of Machine Learning Algorithms Used to Predict Breast Cancer

Ebrahim

Sedky

Mesbah

2023

Data

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Machine learning (ML) was used to develop classification models to predict individual tumor patients’ outcomes. Binary classification defined whether the tumor was malignant or benign. This paper presents a comparative analysis of machine learning algorithms used for breast cancer prediction. This study used a dataset obtained from the National Cancer Institute (NIH), USA, which contains 1.7 million data records. Classical and deep learning methods were included in the accuracy assessment. Classical decision tree (DT), linear discriminant (LD), logistic regression (LR), support vector machine (SVM), and ensemble techniques (ET) algorithms were used. Probabilistic neural network (PNN), deep neural network (DNN), and recurrent neural network (RNN) methods were used for comparison. Feature selection and its effect on accuracy were also investigated. The results showed that decision trees and ensemble techniques outperformed the other techniques, as they both achieved a 98.7% accuracy.

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DeepBreast: Building Optimized Framework for Prognosis of Breast Cancer Classification Based on Computational Intelligence

Cited by 2 publications

References 27 publications

An Explainable Artificial Intelligence Model for the Classification of Breast Cancer

An Explainable Artificial Intelligence Model for the Classification of Breast Cancer

Accuracy Assessment of Machine Learning Algorithms Used to Predict Breast Cancer

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