Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning.
Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning.
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DOI：
10.1155/2021/8387680
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发表时间：
2021
影响因子：
--
通讯作者：
Singh P
中科院分区：
文献类型：
--
作者：
Bharti R;Khamparia A;Shabaz M;Dhiman G;Pande S;Singh P
对心脏病的正确预测可以防止生命威胁，而错误的预测可能同时被证明是致命的。本文将不同的机器学习算法和深度学习应用于UCI机器学习心脏病数据集的结果比较和分析。数据集由用于执行分析的14个主要属性组成。得到了各种有希望的结果，并使用精度和混淆矩阵进行了验证。数据集由一些不相关的特征组成，这些特征使用隔离森林进行处理，并且数据也被归一化以获得更好的结果。并讨论了如何将这项研究与移动设备等多媒体技术相结合。使用深度学习方法，获得了94.2%的准确率。
The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.
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DOI：
10.1109/jbhi.2014.2337752
发表时间：
2014-11-01
影响因子：
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