Breast Cancer Prediction Using Machine Learning Classifiers

2022 5th International Conference on Advances in Science and Technology (ICAST) Pub Date : 2022-12-02 DOI:10.1109/ICAST55766.2022.10039656

Jamal, Jahidul Hasan Antor, Rajneesh Kumar, P. Rani

引用次数: 1

Abstract

Breast cancer is one cancer that is becoming more prevalent every day. It's becoming worse due to a lack of detection. Lowering the death rate may be possible with quick detection. Based on the Wisconsin Breast Cancer dataset, this study suggests a machine learning-based strategy for identifying breast cancer. There were five distinct machine learning algorithms tested. Logistic Regression has given 94.73% accuracy, Decision Tree has 92.98% accuracy, Random Forest has 98.24% accuracy, and Support Vector Machine (SVM) has 96.49% accuracy. Random Forest has given the highest accuracy which is 98.24 %.

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使用机器学习分类器预测乳腺癌

乳腺癌是一种日益流行的癌症。由于缺乏检测，情况变得更糟了。通过快速检测，降低死亡率是可能的。基于威斯康星乳腺癌数据集，本研究提出了一种基于机器学习的乳腺癌识别策略。测试了五种不同的机器学习算法。逻辑回归的准确率为94.73%，决策树的准确率为92.98%，随机森林的准确率为98.24%，支持向量机(SVM)的准确率为96.49%。随机森林给出了最高的准确率，为98.24%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 5th International Conference on Advances in Science and Technology (ICAST)

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