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:2019,28(1):228-232
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基于聚类和XGboost算法的心脏病预测
(1.南京烽火天地通信科技有限公司, 南京 210019;2.武汉邮电科学研究院, 武汉430074)
Heart Disease Prediction Based on Clustering and XGboost
(1.Nanjing FiberHome World Communication Technology Co. Ltd., Nanjing 210019, China;2.Wuhan Research Institute of Posts and Telecommunications, Wuhan 430074, China)
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投稿时间:2018-07-07    修订日期:2018-08-09
中文摘要: 过去十几年来,心脏病发病率在全球一直呈上升趋势且居高不下.所以,如果可以通过计算机手段提取人体相关的体检指标,且通过机器学习的方式来分析不同特征及其权值对于心脏病的影响,对于预测和预防心脏病将起到很关键的作用.因此本文提出一个基于聚类和XGboost算法的预测方法.通过对数据的预处理,区分特征,再通过聚类算法如K-means对数据集聚类分块.最后用XGboost算法进行预测分析.实验结果表明,所提出的基于聚类和XGboost算法的预测方法的可行性和有效性,为就医推荐等应用提供了精准有效的帮助.
中文关键词: 心脏病预测  聚类  机器学习  K-means  XGboost
Abstract:In the past decade, the incidence of heart disease has been on the rise and remains high in the world. If the physical examination indicators related to the human body can be extracted by computer measures, and the influence of different characteristics and their weights on heart disease can be analyzed through machine learning, it will play a key role in predicting and preventing heart disease. Therefore, a prediction method based on clustering and XGboost algorithm is proposed in this study. By preprocessing the data and distinguishing the features, the data sets are clustered by clustering algorithm, such as K-means. Finally, the XGboost algorithm is used to predict and analyze. The experimental results show that the proposed method based on clustering and XGboost algorithm is feasible and effective, which provides accurate and effective help for the application of medical recommendation.
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刘宇,乔木.基于聚类和XGboost算法的心脏病预测.计算机系统应用,2019,28(1):228-232
LIU Yu,QIAO Mu.Heart Disease Prediction Based on Clustering and XGboost.COMPUTER SYSTEMS APPLICATIONS,2019,28(1):228-232

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