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 的最佳特征选择和改进的 Elman-SFO 的分类。


心血管疾病（CVD）的预测是临床数据分析领域的一个关键挑战。在这项研究中，基于最佳特征选择开发了一种有效的心脏病预测。最初，数据预处理过程是使用数据清理、数据转换、缺失值插补和数据标准化来执行的。然后在特征选择过程中采用基于决策函数的混沌樽海鞘群（DFCSS）算法来选择最优特征。然后将选择的属性赋予改进的Elman神经网络（IENN）进行数据分类。这里，旗鱼优化（SFO）算法用于计算IENN的最佳权重值。结合基于DFCSS-IENN的SFO（IESFO）算法可以有效预测心脏病。所提出的 (DFCSS-IESFO) 方法是在 Python 环境中使用两个不同的数据集（例如加州大学欧文分校 (UCI) 克利夫兰心脏病数据集和 CVD 数据集）实现的。仿真结果证明，与支持向量机、K近邻、Elman神经网络、Gaussian Naive等其他分类器相比，该方案对CVD数据集实现了98.7%的高分类准确率，对UCI数据集实现了98%的高分类准确率贝叶斯、逻辑回归、随机森林和决策树。