支持向量机在乳腺癌诊断预测中的应用研究

Pub Date : 2023-06-30 DOI:10.4018/ijhisi.325219
A. Maanav, K. Mithun, T. L. Naparajith, K. Maarvin Abiram Suraj, Regin Bose, Belwin J. Brearley
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引用次数: 0

摘要

本研究探讨了在乳腺癌检测的预测分析模型中使用支持向量机(SVM)学习算法。本研究使用威斯康星州乳腺癌数据集,并使用包括准确性、f1评分、精度和召回率在内的指标来评估模型的性能。将支持向量机模型的性能与其他分类技术(包括逻辑回归、决策树和随机森林)进行了比较。研究结果表明,利用预测分析模型,特别是SVM算法,对乳腺癌的检测是有用的。支持向量机模型具有显著的预测有效性和准确性,是临床医生早期识别和诊断乳腺癌的可行工具。
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Investigating the Prediction of Breast Cancer Diagnosis by Use of Support Vector Machines
This study examines the use of support vector machine (SVM) learning algorithms in predictive analytics models for the detection of breast cancer. This study uses the breast cancer Wisconsin dataset and evaluates the model's performance using measures including accuracy, F1-score, precision, and recall. Comparisons are made between the SVM model's performance and those of alternative classification techniques including logistic regression, decision trees, and random forests. The findings demonstrate the usefulness of utilising predictive analytics models, notably the SVM algorithm, for the detection of breast cancer. The SVM model demonstrated significant predictive effectiveness and accuracy, making it a viable choice of tool for clinicians in the early identification and diagnosis of breast cancer.
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