基于决策树方法的ML乳腺癌分级

Divya Paikaray, G. Jethava
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引用次数: 0

摘要

BC的高死亡率和发病率危及女性患者。因此,乳腺癌的检测方法是必不可少的。逻辑回归、dt、随机森林和CNN预测乳腺癌。预测早期乳腺癌症状需要ML。本研究使用三种ML分类技术。我们将评估每个算法的性能和准确性。分类系统必须仔细管理和预处理不平衡数据。我们将在BC患者数据上训练ML模型。性能和准确性比较确定了此任务的最佳算法。本研究将比较BC分类模型以确定最佳方法。本研究预测了BC分类系统的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ML based with Decision Tree Method for Classifying The Breast Cancer Level
BC's high mortality and morbidity rates endanger female patients. Thus, a breast cancer detection method is essential. Logistic regression, DTs, random forests, and CNN predicted breast cancer. Predicting early breast cancer symptoms requires ML. This study uses three classification ML techniques. We'll evaluate each algorithm's performance and accuracy. Classification systems must carefully manage and preprocess unbalanced data. We'll train ML models on BC patient data. Performance and accuracy comparisons identify the best algorithm for this task. This study will compare BC classification models to determine the optimal approach. This study predicts BC classification system accuracy.
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