SASTRA University, Thanjavur, Tamil Nadu, India.
National Institute of Technology, Meghalaya, India.
J Healthc Eng. 2017;2017:5907264. doi: 10.1155/2017/5907264. Epub 2017 Jul 4.
With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.
随着电子医疗和远程医疗应用的广泛采用，准确、智能的疾病诊断系统备受追捧。近年来，已经提出并测试了许多基于单个机器学习的分类器，几乎可以肯定的是，单个分类器不能有效地对所有疾病进行分类和诊断。这促使最近的一些研究尝试使用集成分类技术达成共识。在本文中，提出了一种使用两种分类器技术（支持向量机 (SVM) 和多层感知器 (MLP) 技术）优化单个分类器参数的混合系统来诊断疾病。我们使用三种最新的进化算法来优化上述分类器的参数，从而产生了六个替代的混合疾病诊断系统，也称为混合智能系统 (HIS)。我们考虑了多个目标，即预测准确性、敏感性和特异性，以评估所提出的混合系统与现有系统的效果。该模型在 11 个基准数据集上进行了评估，结果表明，我们提出的混合诊断系统在疾病预测准确性、敏感性和特异性方面表现更好。进行了相关的统计检验来证实所获得结果的有效性。
