A Supervised Learning Identification System for Prognosis of Breast Cancer

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 ma...

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Bibliographic Details
Published in:Mathematical problems in engineering 2022-08, Vol.2022, p.1-8
Main Authors: Rawat, Vandana, Gulati, Kamal, Kaur, Upinder, Seth, Jitendra Kumar, Solanki, Vikas, Venkatesh, A. Narasima, Singh, Devesh Pratap, Singh, Neelam, Loganathan, Muralidaran
Format: Article
Language:English
Subjects:
Algorithms
Biopsy
Breast cancer
Breastfeeding & lactation
Classification
Data analysis
Datasets
Diagnosis
Machine learning
Mammography
Medical diagnosis
Medical prognosis
Patients
Prognosis
Statistical analysis
Supervised learning
Support vector machines
Unstructured data
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/7459455