Prediction of cardiovascular disease (CVD) is a critical challenge in the area of clinical data analysis. In this study, an efficient heart disease prediction is developed based on optimal feature selection. Initially, the data pre-processing process is performed using data cleaning, data transformation, missing values imputation, and data normalisation. Then the decision function-based chaotic salp swarm (DFCSS) algorithm is used to select the optimal features in the feature selection process. Then the chosen attributes are given to the improved Elman neural network (IENN) for data classification. Here, the sailfish optimisation (SFO) algorithm is used to compute the optimal weight value of IENN. The combination of DFCSS-IENN-based SFO (IESFO) algorithm effectively predicts heart disease. The proposed (DFCSS-IESFO) approach is implemented in the Python environment using two different datasets such as the University of California Irvine (UCI) Cleveland heart disease dataset and CVD dataset. The simulation results proved that the proposed scheme achieved a high-classification accuracy of 98.7% for the CVD dataset and 98% for the UCI dataset compared to other classifiers, such as support vector machine, K-nearest neighbour, Elman neural network, Gaussian Naive Bayes, logistic regression, random forest, and decision tree.

中文翻译：

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高效的心脏病预测-基于使用DFCSS进行最佳特征选择并通过改进的Elman-SFO进行分类。

心血管疾病（CVD）的预测是临床数据分析领域中的关键挑战。在这项研究中，基于最佳特征选择开发了有效的心脏病预测。最初，数据预处理过程是使用数据清理，数据转换，缺失值插补和数据归一化来执行的。然后基于决策函数的混沌蜂群算法在特征选择过程中选择最优特征。然后将选定的属性提供给改进的Elman神经网络（IENN）进行数据分类。在此，使用旗鱼优化（SFO）算法来计算IENN的最佳权重值。基于DFCSS-IENN的SFO（IESFO）算法的组合可有效预测心脏病。所提出的（DFCSS-IESFO）方法是在Python环境中使用两个不同的数据集实现的，例如加利福尼亚大学尔湾分校（UCI）克利夫兰心脏病数据集和CVD数据集。仿真结果表明，与支持向量机，K近邻，Elman神经网络，高斯朴素等其他分类器相比，该方法对CVD数据集和UCI数据集的分类精度均达到98.7％。贝叶斯，逻辑回归，随机森林和决策树。