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Big data analytics for early detection of breast cancer based on machine learning

机译:基于机器学习的乳腺癌早期检测大数据分析

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This paper presents the concept and the modern advances in personalized medicine that rely on technology and review the existing tools for early detection of breast cancer. The breast cancer types and distribution worldwide is discussed. It is spent time to explain the importance of identifying the normality and to specify the main classes in breast cancer, benign or malignant. The main purpose of the paper is to propose a conceptual model for early detection of breast cancer based on machine learning for processing and analysis of medical big dataand further knowledge discovery for personalized treatment. The proposed conceptual model is realized by using Naive Bayes classifier. The software is written in python programming language and for the experiments the Wisconsin breast cancer database is used. Finally, the experimental results are presented and discussed.
机译:本文介绍了个性化医学的概念和现代进步,依赖于技术,并审查现有的早期检测乳腺癌的工具。讨论了全球乳腺癌类型和分布。花时间解释识别正常性的重要性,并在乳腺癌,良性或恶性肿瘤中指定主要类别。本文的主要目的是提出基于机器学习的乳腺癌早期检测概念模型,用于处理和分析医疗大数据和个性化治疗的进一步知识发现。通过使用Naive Bayes分类器实现了所提出的概念模型。该软件以Python编程语言编写,实验使用威斯康辛乳腺癌数据库。最后,提出和讨论了实验结果。

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