Mammography images classification system based texture analysis and multi class support vector machine

Abdullah, Ahmed Khalid; Azawi, Raghad Majeed; Ibrahim, Ibrahim Tareq; Ajwad, Asmaa Abbas

doi:10.1063/5.0110733

Cited by 1 publication

(1 citation statement)

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“…The model achieved 96.15% accuracy. In their study, Abdullah et al [76] achieved an accuracy of 98% by using a multi-class SVM. The performance of the SVM model throughout the numerous studies is shown in Table 2.…”

Section: Svmmentioning

confidence: 98%

Breast Cancer-Risk Factors and Prediction Using Machine-Learning Algorithms and Data Source: A Review of Literature

Alsabry,

Algabri,

Ahsan

2023

jast

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Breast cancer (BC) is a major health concern worldwide. It is a complex and multifactorial disease, and identifying its risk factors is crucial for early detection and effective treatment. This review article provides an overview of the literature on breast cancer risk factors, data sources, and machine learning algorithms for prediction. The paper discusses the various risk factors associated with breast cancer, including age, family history, lifestyle choices, and environmental factors. Additionally, the article explores the different data sources used in breast cancer research, including clinical data, genomic data, and lifestyle data. The paper then reviews the different machine-learning algorithms used for breast cancer prediction, including supervised and unsupervised learning. The performance of ML algorithms in predicting BC risk using different data sources was assessed. This review provides valuable insights into the current state of research on BC risk factors and prediction using ML algorithms and data sources, and the findings will be useful for healthcare professionals and researchers working in the field of BC.

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Section: Svmmentioning

confidence: 98%