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Accurate Heart Disease Prediction via Improved Stacking Integration Algorithm

机译：通过改进的堆叠集成算法预测精确的心脏病预测

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摘要

The stacking algorithm has better generalization ability than other learning algorithms, and can flexibly handle different tasks. The basic model of this algorithm uses heterogeneous learning devices (different types of learning devices), but for each data set in K-fold cross validation, the learners used are homogeneous (the same type of learner). Considering the neglect of the precision difference by a homogeneous heterotopic learner, the accuracy difference weighting method is proposed to improve the traditional stacking algorithm. In the first layer of the traditional stacking algorithm, the algorithm is weighted according to the prediction accuracy, that is, the output of the test set of the first layer is weighted by the weight calculated with the obtained precision, and the weighted result input into the element learner is taken as the feature. As one of the diseases with the highest incidence and mortality, the effective prediction of heart disease can provide an important basis for assisting diagnosis and enhancing the survival rate of patients. In this article, the improved stacking integration algorithm was used to construct a two-layer classifier model to predict heart disease. The experimental results show that the algorithm can effectively improve the prediction accuracy of heart disease through the verification of other heart disease data sets, and it is found that the stacking algorithm has better generalization performance. (C) 2021 Society for Imaging Science and Technology.
机译：堆叠算法具有比其他学习算法更好的泛化能力，并且可以灵活地处理不同的任务。该算法的基本模型使用异构学习设备（不同类型的学习设备），但对于k折交叉验证中的每个数据，使用的学习者是同类的（相同类型的学习者）。考虑到均匀异相学习者忽视精度差异，提出了精度差异加权方法来改善传统堆叠算法。在传统堆叠算法的第一层中，根据预测精度加权算法，即，第一层的测试集的输出被通过获得的精度计算的权重，加权结果输入元素学习者作为特征。作为发病率和死亡率最高的疾病之一，心脏病的有效预测可以为协助诊断和提高患者的存活率提供重要依据。在本文中，改进的堆叠积分算法用于构建双层分类器模型以预测心脏病。实验结果表明，该算法通过验证其他心脏病数据集可以有效提高心脏病的预测准确性，并且发现堆叠算法具有更好的泛化性能。 （c）2021年成像科技协会。

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  • 来源
    《Journal of Imaging Science and Technology》 |2021年第3期|030408.1-030408.9|共9页
  • 作者

    Jia Hua-ping; Zhao Jun-long; Jun-Liu; Min-Zhang; Sun Wei-Xi;

  • 作者单位

    Weinan Normal Univ Weinan 714099 Shaanxi Peoples R China;

    Weinan Cent Hosp Weinan 714000 Shaanxi Peoples R China;

    Weinan Cent Hosp Weinan 714099 Shaanxi Peoples R China|Natl Local Joint Engn Res Ctr Cultural Heritage D Xian 710069 Peoples R China;

    Weinan Cent Hosp Weinan 714099 Shaanxi Peoples R China|Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China;

    Weinan Cent Hosp Weinan 714099 Shaanxi Peoples R China;

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  • 正文语种 eng
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