An experimental study for breast cancer prediction algorithms

Al-Shargabi, Bassam; Al-Shami, Fida'a

doi:10.1145/3368691.3368703

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…Six supervised learning algorithms were examined for their prognosis and detection capacities in the diagnosis of breast cancer [70,71]. In this dataset, a maximum of 569 instances were utilized [72].…”

Section: Resultsmentioning

confidence: 99%

A Supervised Learning Identification System for Prognosis of Breast Cancer

Rawat

Gulati

Kaur

et al. 2022

Mathematical Problems in Engineering

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Breast cancer is one of the most dangerous cancers, accounting for a large number of fatalities each year. It is the leading cause of mortality among women globally. It is getting a lot of interest in the scientific community because of its possible life-threatening danger. As a consequence, many machine learning methods (MLMs) have been modified to provide the best results for early diagnosis of this malignancy. Machine learning methods (MLMs) offer several beneficial implications in breast cancer, including early prognosis, detection, and diagnosis. Compared to traditional statistical analysis, machine learning methods (MLMs) have the capacity to improve the analysis of various health data, such as unstructured, complicated, and noisy data. With the demanding prevalence of breast cancer and the arrival of “data reformation,” it is thus imperative to mention the ethical consequences of machine learning (ML) on society and cancer care. It offers conclusively strong tools, smart methods, and efficient algorithms that can help in the prognosis of breast cancer. The focus of this review is on supervised techniques such as classification and regression that may be implemented and used for breast cancer data analysis. Some supervised learning methods like Naive Bayes, AdaBoost, and support vector machine are presented in this work in the early identification of breast cancer. These algorithms have been analyzed for their accuracy and efficiency using various assessment metrics and methods.

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Section: Resultsmentioning

confidence: 99%

A Supervised Learning Identification System for Prognosis of Breast Cancer

Rawat

Gulati

Kaur

et al. 2022

Mathematical Problems in Engineering

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A Review of Machine Learning Algorithms on Different Breast Cancer Datasets

Sweetlin,

Saudia

2023

Lecture Notes in Electrical Engineering

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Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping Review

Carrasco,

Tomalá,

Ramírez Meza

et al. 2024

ACM Comput. Surv.

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The problem arises from the lack of sufficient and comprehensive information about the necessary computer techniques. These techniques are crucial for developing information systems that assist doctors in diagnosing breast cancer, especially those related to positron emission tomography and computed tomography (PET/CT). Despite global efforts in breast cancer prevention and control, the scarcity of literature poses an obstacle to a complete understanding in this area of interest. The methodologies studied were systematic mapping and systematic literature review. For each article, the journal, conference, year of publication, dataset, breast cancer characteristics, PET/CT processing techniques, metrics and diagnostic yield results were identified. Sixty-four articles were analyzed, 44 (68.75%) belong to journals and 20 (31.25%) belong to the conference category. A total of 102 techniques were identified, which were distributed in preprocessing with 7 (6.86%), segmentation with 15 (14.71%), feature extraction with 15 (14.71%) and classification with 65 (63.73%). The techniques with the highest incidence identified in each stage are: Gaussian Filter, SLIC, Local Binary Pattern and Support Vector Machine with 4, 2, 7 and 35 occurences respectively. Support Vector Machine is the predominant technique in the classification stage, due to the fact that Artificial Intelligence is emerging in medical image processing and health care to make expert systems increasingly intelligent and obtain favorable results.

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An experimental study for breast cancer prediction algorithms

Cited by 5 publications

References 17 publications

A Supervised Learning Identification System for Prognosis of Breast Cancer

A Supervised Learning Identification System for Prognosis of Breast Cancer

A Review of Machine Learning Algorithms on Different Breast Cancer Datasets

Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping Review

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