论文标题
基于改进的合奏学习的冠心病诊断
Coronary Heart Disease Diagnosis Based on Improved Ensemble Learning
论文作者
论文摘要
在对冠心病进行适当治疗之前，需要进行准确的诊断。许多研究人员提出了基于机器学习的方法，以提高冠心病诊断的准确性。合奏学习和级联概括是可用于提高学习算法的概括能力的方法之一。这项研究的目的是基于集合学习和级联概括开发心脏病诊断方法。在本研究中提出了具有松散耦合策略的级联概括方法。 C4。 5和开膛手算法用作元级算法，而天真的贝叶斯用作基础算法。评估包装和随机子空间以构建合奏。将混合级联集合方法与非汇总模式和非cascade模式下的学习算法进行比较。该方法还与旋转林进行了比较。根据评估结果，混合级联合奏方法证明了给定心脏病诊断病例的最佳结果。进行了准确性和多样性评估，以分析级联策略的影响。根据结果，集合中分类器的准确性增加，但多样性降低。
Accurate diagnosis is required before performing proper treatments for coronary heart disease. Machine learning based approaches have been proposed by many researchers to improve the accuracy of coronary heart disease diagnosis. Ensemble learning and cascade generalization are among the methods which can be used to improve the generalization ability of learning algorithm. The objective of this study is to develop heart disease diagnosis method based on ensemble learning and cascade generalization. Cascade generalization method with loose coupling strategy is proposed in this study. C4. 5 and RIPPER algorithm were used as meta-level algorithm and Naive Bayes was used as baselevel algorithm. Bagging and Random Subspace were evaluated for constructing the ensemble. The hybrid cascade ensemble methods are compared with the learning algorithms in non-ensemble mode and non-cascade mode. The methods are also compared with Rotation Forest. Based on the evaluation result, the hybrid cascade ensemble method demonstrated the best result for the given heart disease diagnosis case. Accuracy and diversity evaluation was performed to analyze the impact of the cascade strategy. Based on the result, the accuracy of the classifiers in the ensemble is increased but the diversity is decreased.
