IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework
IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework
复制标题
DOI：
10.1016/j.jbi.2015.12.001
复制
发表时间：
2016-02-01
影响因子：
4.5
通讯作者：
Khan, Farhan Hassan
中科院分区：
文献类型：
--
作者：
Bashir, Saba;Qamar, Usman;Khan, Farhan Hassan
准确性在医疗领域起着至关重要的作用，因为它关系到个人的生命。已经使用机器学习技术对疾病分类和预测进行了广泛的研究。然而，对于哪种分类器产生最佳结果没有一致意见。对于特定的数据集，特定的分类器可能比其他分类器更好，但另一个分类器可能对其他数据集表现更好。分类器的扩充已被证明是提高分类精度的有效方法。在这项研究中，我们提出了一个集成框架与多层分类使用增强装袋和优化加权。所提出的模型被称为“HM BagMoov”克服了传统的性能瓶颈的限制，利用七个异构分类器的合奏。该框架在五个不同的心脏病数据集，四个乳腺癌数据集，两个糖尿病数据集，两个肝病数据集和一个肝炎数据集上进行了评估。结果分析表明，集成框架取得了最高的准确性，灵敏度和F-测量时，与个人分类器的所有疾病。除此之外，与最先进的技术相比，集成框架还实现了最高的准确性。一个名为“智能健康”的应用程序也是基于所提出的模型，可用于医院/医生的诊断建议。(C)2015 Elsevier Inc. All rights reserved.
Accuracy plays a vital role in the medical field as it concerns with the life of an individual. Extensive research has been conducted on disease classification and prediction using machine learning techniques. However, there is no agreement on which classifier produces the best results. A specific classifier may be better than others for a specific dataset, but another classifier could perform better for some other data set. Ensemble of classifiers has been proved to be an effective way to improve classification accuracy. In this research we present an ensemble framework with multi-layer classification using enhanced bagging and optimized weighting. The proposed model called "HM-BagMoov" overcomes the limitations of conventional performance bottlenecks by utilizing an ensemble of seven heterogeneous classifiers. The framework is evaluated on five different heart disease datasets, four breast cancer datasets, two diabetes datasets, two liver disease datasets and one hepatitis dataset obtained from public repositories. The analysis of the results show that ensemble framework achieved the highest accuracy, sensitivity and F-Measure when compared with individual classifiers for all the diseases. In addition to this, the ensemble framework also achieved the highest accuracy when compared with the state of the art techniques. An application named "IntelliHealth" is also developed based on proposed model that may be used by hospitals/doctors for diagnostic advice. (C) 2015 Elsevier Inc. All rights reserved.