当前位置: X-MOL 学术Interdiscip. Sci. Comput. Life Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Enhanced Evolutionary Feature Selection and Ensemble Method for Cardiovascular Disease Prediction
Interdisciplinary Sciences: Computational Life Sciences ( IF 4.8 ) Pub Date : 2021-05-14 , DOI: 10.1007/s12539-021-00430-x
V Jothi Prakash 1 , N K Karthikeyan 2
Affiliation  

Cardiovascular Disease (CVD) is one among the main factors for the increase in mortality rate worldwide. The analysis and prediction of this disease is yet a highly formidable task in medical data analysis. Recent advancements in technology such as Big Data, Artificial Intelligence and the need for automated models have paved the way for developing a more reliable and efficient model for predicting heart disease. Several researches have been carried out in predicting heart diseases but the focus on choosing the important attributes that play a significant role in predicting CVD is inadequate. Hence the choice of right features for the classification and the diagnosis of the heart disease is important. The core aim of this work is to identify and select the important features and machine learning methodologies that can enhance the prediction capability of the classification models for accurately predicting CVD. The results show that the proposed enhanced evolutionary feature selection with the hybrid ensemble model outperforms the existing approaches in terms of precision, recall and accuracy. The experimental outcomes show that the proposed approach attains the maximum classification accuracy of 93.65% for statlog dataset, 82.81% for SPECTF dataset and 84.95% for coronary heart disease dataset. The proposed classification model performance is demonstrated using ROC curve against state-of-the-art methods in machine learning.

Graphic Abstract



中文翻译:

心血管疾病预测的增强进化特征选择和集成方法

心血管疾病 (CVD) 是全球死亡率上升的主要因素之一。这种疾病的分析和预测仍然是医学数据分析中一项非常艰巨的任务。大数据、人工智能等技术的最新进展以及对自动化模型的需求,为开发更可靠、更有效的心脏病预测模型铺平了道路。在预测心脏病方面已经进行了几项研究,但对选择在预测 CVD 中起重要作用的重要属性的关注是不够的。因此,为心脏病的分类和诊断选择正确的特征很重要。这项工作的核心目标是识别和选择可以增强分类模型预测能力的重要特征和机器学习方法,以准确预测 CVD。结果表明,所提出的混合集成模型的增强进化特征选择在精度、召回率和准确性方面优于现有方法。实验结果表明,所提出的方法在 statlog 数据集上达到了 93.65% 的最大分类准确率,在 SPECTF 数据集上达到了 82.81%,在冠心病数据集上达到了 84.95%。所提出的分类模型性能使用 ROC 曲线与机器学习中的最新方法进行对比。结果表明,所提出的混合集成模型的增强进化特征选择在精度、召回率和准确性方面优于现有方法。实验结果表明,所提出的方法在 statlog 数据集上达到了 93.65% 的最大分类准确率,在 SPECTF 数据集上达到了 82.81%,在冠心病数据集上达到了 84.95%。所提出的分类模型性能使用 ROC 曲线与机器学习中的最新方法进行对比。结果表明,所提出的混合集成模型的增强进化特征选择在精度、召回率和准确性方面优于现有方法。实验结果表明,所提出的方法在 statlog 数据集上达到了 93.65% 的最大分类准确率,在 SPECTF 数据集上达到了 82.81%,在冠心病数据集上达到了 84.95%。所提出的分类模型性能使用 ROC 曲线与机器学习中的最新方法进行对比。

图形摘要

更新日期:2021-05-14
down
wechat
bug