Diagnosis of heart disease is a difficult job, and researchers have designed various intelligent diagnostic systems for improved heart disease diagnosis. However, low heart disease prediction accuracy is still a problem in these systems. For better heart risk prediction accuracy, we propose a feature selection method that uses a floating window with adaptive size for feature elimination (FWAFE). After the feature elimination, two kinds of classification frameworks are utilized, i.e., artificial neural network (ANN) and deep neural network (DNN). Thus, two types of hybrid diagnostic systems are proposed in this paper, i.e., FWAFE-ANN and FWAFE-DNN. Experiments are performed to assess the effectiveness of the proposed methods on a dataset collected from Cleveland online heart disease database. The strength of the proposed methods is appraised against accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and receiver operating characteristics (ROC) curve. Experimental outcomes confirm that the proposed models outperformed eighteen other proposed methods in the past, which attained accuracies in the range of 50.00–91.83%. Moreover, the performance of the proposed models is impressive as compared with that of the other state-of-the-art machine learning techniques for heart disease diagnosis. Furthermore, the proposed systems can help the physicians to make accurate decisions while diagnosing heart disease.

中文翻译：

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使用新的特征选择方法进行特征细化和神经网络进行分类的心脏病风险预测

心脏病的诊断是一项艰巨的工作，研究人员设计了各种智能诊断系统来改善心脏病的诊断。然而，在这些系统中，低心脏病预测准确性仍然是一个问题。为了获得更好的心脏风险预测准确性，我们提出了一种特征选择方法，该方法使用具有自适应大小的浮动窗口进行特征消除（FWAFE）。特征消除后，利用了两种分类框架，即人工神经网络（ANN）和深度神经网络（DNN）。因此，本文提出了两种类型的混合诊断系统，即FWAFE-ANN和FWAFE-DNN。从克利夫兰在线心脏病数据库收集的数据集上进行实验以评估所提出方法的有效性。针对准确性，灵敏度，特异性，马修斯相关系数（MCC）和接收器工作特性（ROC）曲线，评估了所提出方法的强度。实验结果证实，所提出的模型在过去优于其他18种提出的方​​法，其准确度在50.00–91.83％的范围内。此外，与其他用于心脏病诊断的最新机器学习技术相比，所提出模型的性能令人印象深刻。此外，提出的系统可以帮助医生在诊断心脏病的同时做出准确的决定。实验结果证实，所提出的模型在过去优于其他18种提出的方​​法，其准确度在50.00–91.83％的范围内。此外，与其他用于心脏病诊断的最新机器学习技术相比，所提出模型的性能令人印象深刻。此外，提出的系统可以帮助医生在诊断心脏病的同时做出准确的决定。实验结果证实，所提出的模型在过去优于其他18种提出的方​​法，其准确度在50.00–91.83％的范围内。此外，与其他用于心脏病诊断的最新机器学习技术相比，所提出模型的性能令人印象深刻。此外，提出的系统可以帮助医生在诊断心脏病的同时做出准确的决定。