论文标题
智能医疗保健监测环境中冠状动脉疾病预测预测的智能决策合奏投票模型
An Intelligent Decision Support Ensemble Voting Model for Coronary Artery Disease Prediction in Smart Healthcare Monitoring Environments
论文作者
论文摘要
冠状动脉疾病（CAD）是全球最常见的心脏病之一，会导致残疾和经济负担。这是世界上主要也是最严重的死亡原因，在低收入国家和中等收入国家中报告了约80％的死亡。 CAD的首选和最精确的诊断工具是血管造影，但具有侵入性，昂贵且技术要求。但是，研究界通过使用机器学习（ML）方法的计算机辅助诊断CAD越来越感兴趣。这项工作的目的是提出一个基于ML算法的电子诊断工具，该工具可用于智能医疗保健监测系统中。我们应用了最精确的机器学习方法，这些方法在文献中显示出优异的结果，例如Randomforest，XGBoost，MLP，J48，Adaboost，NaiveBayes，LogitBoost，KNN。每个分类器都可以在不同的数据集上有效。因此，使用多数投票的合奏模型旨在利用良好的单个分类器，合奏学习旨在结合多个单个分类器的预测，以优先，特异性，敏感性和准确性和准确性，以比单个分类器更高的性能；此外，我们还通过基于交叉验证技术的最有效和著名的合奏模型将我们提出的模型标记为基于装袋，堆叠方法，实验结果证实，基于前3个分类器的集合多数投票方法：Multilayerperceptron，commanyforrest，commanforefrest，anderforest，complyforest，and odaboost，All ofersefers the其他copters and of其他精确度和588888888888888。这项研究表明，上述大多数投票集合方法是用于预测和检测冠状动脉疾病的最准确的机器学习分类方法。
Coronary artery disease (CAD) is one of the most common cardiac diseases worldwide and causes disability and economic burden. It is the world's leading and most serious cause of mortality, with approximately 80% of deaths reported in low- and middle-income countries. The preferred and most precise diagnostic tool for CAD is angiography, but it is invasive, expensive, and technically demanding. However, the research community is increasingly interested in the computer-aided diagnosis of CAD via the utilization of machine learning (ML) methods. The purpose of this work is to present an e-diagnosis tool based on ML algorithms that can be used in a smart healthcare monitoring system. We applied the most accurate machine learning methods that have shown superior results in the literature to different medical datasets such as RandomForest, XGboost, MLP, J48, AdaBoost, NaiveBayes, LogitBoost, KNN. Every single classifier can be efficient on a different dataset. Thus, an ensemble model using majority voting was designed to take advantage of the well-performed single classifiers, Ensemble learning aims to combine the forecasts of multiple individual classifiers to achieve higher performance than individual classifiers in terms of precision, specificity, sensitivity, and accuracy; furthermore, we have benchmarked our proposed model with the most efficient and well-known ensemble models, such as Bagging, Stacking methods based on the cross-validation technique, The experimental results confirm that the ensemble majority voting approach based on the top 3 classifiers: MultilayerPerceptron, RandomForest, and AdaBoost, achieves the highest accuracy of 88,12% and outperforms all other classifiers. This study demonstrates that the majority voting ensemble approach proposed above is the most accurate machine learning classification approach for the prediction and detection of coronary artery disease.
