An Early Detection of Breast Cancer Using Hybrid Ensemble Classifier

Arumbaka Srinivasa Rao, Yamini Tondepu, Siva Kumari N, Ch. Prasad
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引用次数: 1

Abstract

In the past few years, India has reported 30% of breast cancer cases, and this number is likely to increase. In India, a woman is diagnosed with breast cancer every two minutes and dies every nine minutes. Women who are diagnosed and treated early can have a better chance for survival. This article offers a new machine learning-based strategy for diagnosing breast cancer known as an Enhanced ensembled classification model. Further, this research work has conducted an experimental analysis to check the validity of the dataset extracted from the Kaggle repository. When compared to other algorithms such as Logistic Regression and SVM, the proposed model provides more accurate and effective outcomes when implemented and compared with existing methods.
基于混合集成分类器的乳腺癌早期检测
在过去几年中,印度报告了30%的乳腺癌病例,这一数字可能会增加。在印度,每两分钟就有一名妇女被诊断出患有乳腺癌,每九分钟就有一名妇女死亡。早期诊断和治疗的妇女有更好的生存机会。本文提供了一种新的基于机器学习的乳腺癌诊断策略,称为增强集成分类模型。此外,本研究工作还进行了实验分析,验证了从Kaggle库中提取的数据集的有效性。与逻辑回归、支持向量机等算法相比,本文提出的模型在实现过程中比现有方法提供了更准确、更有效的结果。
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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