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.

中文翻译：

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结合机器学习和深度学习预测心脏病

对心脏病的正确预测可以预防生命威胁，而错误的预测同时也可以证明是致命的。在本文中，应用不同的机器学习算法和深度学习来比较 UCI 机器学习心脏病数据集的结果和分析。该数据集包含用于执行分析的 14 个主要属性。取得了各种有希望的结果，并使用准确性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处理，并且数据也被归一化以获得更好的结果。还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。