Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
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DOI：
10.1109/access.2020.3001149
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Saboor, Abdus
中科院分区：
文献类型：
--
作者：
Li, Jian Ping;Ul Haq, Amin;Saboor, Abdus
心脏病是一种复杂的疾病，全球许多人患有这种疾病。及时有效地识别心脏病在医疗保健中发挥着关键作用，特别是在心脏病学领域。在这篇文章中，我们提出了一个有效和准确的系统来诊断心脏病，该系统是基于机器学习技术。该系统是基于分类算法，包括支持向量机，逻辑回归，人工神经网络，K-最近邻，Na& x 00 EF;VE湾，决策树，而标准的功能选择算法，如救济，最小冗余最大相关性，最小绝对收缩选择算子和本地学习去除无关和冗余的功能。提出了一种新的快速条件互信息特征选择算法来解决特征选择问题。特征选择算法用于特征选择，以提高分类精度，减少分类系统的执行时间。此外，留一个主题交叉验证方法已被用于学习模型评估和超参数调整的最佳实践。性能测量指标用于评估分类器的性能。分类器的性能已被检查的特征选择算法所选择的功能。实验结果表明，所提出的特征选择算法（FCMIM）与分类器支持向量机设计一个高层次的智能系统，以识别心脏疾病是可行的。建议的诊断系统（FCMIM-SVM）取得了良好的精度相比，以前提出的方法。此外，所提出的系统可以很容易地在医疗保健中实现，用于识别心脏病。
Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Na& x00EF;ve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.