Heart-Disease Prediction Method Using Random Forest and Genetic Algorithms

Mohamed G. El-Shafiey, Ahmed M. Hagag, E. El-Dahshan, Manal A. Ismail
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Abstract

Today, heart-disease is one of the most significant causes of mortality in the world. Thus, the prediction of heart-disease is a critical challenge in the area of healthcare systems. In this study, we aim to select the optimal features that can increase the accuracy of heart-disease prediction. A feature-selection algorithm, which is based on genetic algorithm (GA) and random forest (RF), is proposed to increase the accuracy of RF-based classification and determine the optimal heart-disease-prediction features. The performance of the proposed approach is validated via evaluation metrics, namely, accuracy, specificity, sensitivity, and area under the ROC curve by using a public dataset from the University of California, namely, Cleveland. The experimental results confirm that the proposed approach attained the high heart-disease-prediction accuracies of 95.6% on the Cleveland dataset. Furthermore, the proposed approach outperformed other state-of-the-art prediction methods.
基于随机森林和遗传算法的心脏病预测方法
今天,心脏病是世界上最重要的死亡原因之一。因此,心脏病的预测是医疗保健系统领域的一个关键挑战。在这项研究中,我们的目标是选择可以提高心脏病预测准确性的最佳特征。为了提高基于遗传算法(GA)和随机森林(RF)的分类准确率,确定最佳的心脏病预测特征,提出了一种基于遗传算法(GA)和随机森林(RF)的特征选择算法。通过使用来自加州大学克利夫兰分校的公共数据集,通过评估指标,即准确性、特异性、敏感性和ROC曲线下面积,验证了所提出方法的性能。实验结果证实,该方法在Cleveland数据集上达到了95.6%的心脏病预测准确率。此外,所提出的方法优于其他最先进的预测方法。
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