使用监督学习的心脏病预测和诊断

Q4 Environmental Science
Ijaz Hussain, Wajiha Safat
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引用次数: 0

摘要

现有的临床诊断数据往往被放大,但现有的工具是不够有效的决策。数据挖掘技术为临床诊断提供了一种面向用户的方法,减少了风险因素。为了提高临床诊断,特别是对心脏病的诊断,九种不同的数据挖掘技术被应用于分类和聚类。为了更好地预测,我们比较了所有这些技术。尽管最近所有的研究努力,文献缺乏在多个数据集上应用多种技术进行心脏病预测;这有助于决策。特别地,本研究通过比较具有14个属性和不同数量实例的四个数据集,增强了多数据分析技术。另一个挑战是如何提高决策过程的准确性。我们的研究结果表明,使用SMO和回归分类对所有数据集的预测精度都有较好的提高,两者之间存在显著差异。因此,本研究进一步有助于整合临床决策支持,从而减少医疗差错,提高患者安全,减少不必要的实践变化,提高患者康复。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Heart Diseases Prediction and Diagnosis using Supervised Learning
The existing data for clinical diagnosis are often enlarged, but available tools are not efficient enough for decision making. Data mining techniques provide a user-oriented approach for clinical diagnosis and reduce risk factors. To improve clinical diagnosis, particularly for heart diseases, nine different data mining techniques have been applied for classification and clustering. We compare all these techniques for better prediction. Despite all recent research efforts, the literature lacks the application of multiple techniques on multiple data sets for heart disease prediction; which helps in decision making. In particular, this study is the augmentation of techniques for multiple data analysis by comparing four datasets with 14 attributes and a different number of instances. Another challenge is how to increase the accuracy of the decision-making process. Our research findings predict the better accuracy by using SMO and classification via regression for all data sets which shows the significant difference. Consequently, this research further helps to integrate the clinical decision support, thereby reducing medical errors, enhance patient safety, decrease unwanted practice variation, and improve patient recovery.
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来源期刊
Iranian Journal of Botany
Iranian Journal of Botany Environmental Science-Ecology
CiteScore
0.80
自引率
0.00%
发文量
0
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