支持向量机在结肠动力无创诊断中的应用

在结肠动力疾病的无创诊断中,由于存在临床样本数量有限、病人个体的特异性和数据本身的噪声等因素的影响,要进行非常准确的分类诊断是困难的。支持向量机是在统计学习理论基础上发展而来的一种新的通用学习方法,较好地解决了有限样本的学习分类问题。文章采用非线性支持向量机分类算法,构造支持向量机分类器,并将其应用于结肠动力的分类诊断。非线性支持向量机取得了较高的准确率,表明支持向量机在结肠动力疾病的分类诊断中有很大的应用潜力。     

融合特征选取和学习策略的支持向量机研究

支持向量机是重要的机器学习方法之一,已成功解决了许多实际的分类问题。围绕如何提高支持向量机的分类精度与训练效率,以分类过程为主线,主要综述了在训练支持向量机之前不同的特征选取方法与学习策略。在此基础上,比较了不同的特征选取方法SFS,IWSS,IWSSr以及BARS的分类精度,分析了主动学习策略与支持向量机融合后获得的分类器在测试集上的分类精度与正确率/召回率平衡点两个性能指标。实验结果表明,包装方法与过滤方法相结合的特征选取方法能有效提高支持向量机的分类精度和减少训练样本量;在标签数据较少的情况下,主动学习能达到更好的分类精度,而为了达到相同的分类精度,被动学习需要的样本数量必须要达到主动学习的6倍。     

基于统计学习理论的支持向量机的分类方法

支持向量机是一种新型机器学习方法,由于其出色的学习性能,该技术已成为机器学习领域新的研究热点。介绍用于分类的支持向量机的统计学习理论基础,在此基础上提出了支持向量机的分类算法,讨论了支持向量机存在的问题,对用于分类的支持向量机的应用前景进行了展望。     

基于统计学习理论的支持向量机的分类方法

支持向量机是一种新型机器学习方法，由于其出色的学习性能，该技术已成为机器学习领域新的研究热点。介绍用于分类的支持向量机的统计学习理论基础，在此基础上提出了支持向量机的分类算法，讨论了支持向量机存在的问题，对用于分类的支持向量机的应用前景进行了展望。     

基于代价敏感支持向量机的推荐系统托攻击检测方法

基于标准支持向量机的托攻击检测方法不能体现由于用户误分代价不同对分类效果带来的影响,提出了一种基于代价敏感支持向量机的托攻击检测新方法,该方法在代价敏感性学习机制下引入支持向量机作为分类工具,对支持向量机输出进行后验概率建模,建立了基于类别隶属度的动态代价函数,更准确地反映不同样本的分类代价,在此基础上设计了代价敏感支持向量机分类器。将该分类器应用在推荐系统托攻击检测中,并与标准的支持向量机方法、代价敏感支持向量机方法进行比较,实验结果表明,本方法可以更精确地控制代价敏感性,进一步提高对攻击用户的检测精度,降低总体的误分类代价。     

基于支持向量机故障诊断方法

支持向量机是一种基于统计学习理论的机器学习算法,它能在训练样本很少的情况下达到很好的分类效果.本文以双螺杆挤出机为例,介绍了基于支持向量机的多故障分类器,探讨了"成对分类"与"一类对多类"两种多类分类算法的应用.诊断实例表明,基于支持向量机的多故障分类器对设备故障具有很好的分类效果.     

SVM算法及其在多类字母图象识别中的应用

支持向量机是一种新的统计学习算法,其学习原则是使结构风险最小,与经典的学习方法的经验风险最小原则不同,这使得支持向量机具有很强的泛化能力。因为支持向量机算法是一个凸二次优化问题,能够保证所求的局部最优解就是全局最优解。目前,研究的绝大多数是两类问题。然而,即使我们能够将两类问题正确分类,也不能意味着实际应用中多类分类问题的解决。在这篇文章中,我们介绍了支持向量机算法,并且通过多类字母图象分类问题说明支持向量机算法在多类分类问题中的应用。     

滚动轴承技术故障诊断的支持向量机方法研究

针对当前故障诊断中几种常用方法的不足,首次提出将支持向量机方法应用于滚动轴承技术故障诊断。该文提出的两种算法其核心均是利用支持向量机方法对样本进行分类。支持向量机方法基于小学习样本条件下,通过寻求结构风险最小,以期获得良好的分类效果和泛化能力。两种实验结果表明,在选用合适核函数及参数条件下,支持向量机具有学习速度快、诊断正确率高的优良性能,这一结论表明了该文所提出方法的优越性。     

一种适合于增量学习的支持向量机的快速循环算法

当样本数量大到计算机内存中放不下时，常规支持向量机方法就失去了学习能力，为了解决这一问题，提高支持向量机的训练速度，文章分析了支持向量机分类的本质特征，根据支持向量机分类仅与支持向量有关的特点，提出了一种适合于支持向量机增量学习的快速循环算法(PFI-SVM)，提高了支持向量机的训练速度和大样本学习的能力，而支持向量机的分类能力不受任何影响，取得了较好的效果。     

最小二乘支持向量机在睡眠打鼾诊断中的应用

支持向量机是数据挖掘和机器学习领域中的重要方法之一,最小二乘支持向量机是支持向量机学习算法的重要扩展,在训练速度方面有明显优势。对支持向量机现有的多类分类算法（一对一方法、一对多方法、纠错输出编码方法和最小输出编码方法）引入了最小二乘支持向量机,并应用于睡眠打鼾疾病的诊断预测中,取得了较好的效果。     

支持向量机分类器在医疗诊断中的应用研究

在医疗诊断中，常根据病人的多项病理检测结果进行诊断，由于存在个体的差异和数据本身的噪声，所以要准确的诊断是困难的，支持向量机是统计学习理论基础上发展而来的一种新的通用学习方法，具有很多独特的优点。该文介绍了支持向量机非线性分类算法，选取径向基核函数，构造了支持向量机非线性分类器，并将其应用于心脏病诊断，所用数据来自UCI benchmark数据集，与其它方法相比，取得了较高的准确率，结果表明支持向量机在医疗诊断中有很大的应用潜力。     

一种融合二维和三维卷积网络的两阶段冠状动脉分 割方法

心血管疾病是一种严重危害公众健康的重大疾病。与其他心血管疾病相比,冠心病是导致死亡的最主要原因,精确的冠状动脉分割对冠心病的治疗有重要意义。目前,深度学习已经广泛应用于医学影像领域,然而,像冠状动脉这样的小物体的分割仍然是一大挑战。针对冠状动脉精确分割的需求,该研究提出了一种融合二维和三维卷积网络的方案,利用骨架作为桥梁,结合二维和三维卷积网络,扩大了卷积网络的信息接受域。与其他深度学习方法相比,该方法在敏感度、Dice 系数、ROC 曲线下方的面积、豪斯多夫距离上均有一定程度的提升,且可以检测其他方法无法识别的冠状动脉,一定程度上解决了血管断连和血管缺失等问题。     

Coronary artery disease prediction method using linear and nonlinear feature of heart rate variability in three recumbent postures

In present study, we proposed not only a novel methodology useful in developing the various features of heart rate variability (HRV), but also a suitable prediction model to enhance the reliability of medical examinations and treatments for coronary artery disease. In order to develop the various features of HRV, we analyzed HRV for three recumbent postures. The interaction effects between the recumbent postures and groups of normal people and heart patients were observed based on linear and nonlinear features of HRV. Forty-three control subjects and 64 patients with coronary artery disease participated in this study. In order to extract various features, we tested five classification methods and evaluated performance of classifiers. As a result, SVM and CMAR (gave about 72–88% goodness of accuracy) outperformed the other classifiers.     

Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System

Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System (MKL with ANFIS) based deep learning method is proposed in this paper for heart disease diagnosis. The proposed MKL with ANFIS based deep learning method follows two-fold approach. MKL method is used to divide parameters between heart disease patients and normal individuals. The result obtained from the MKL method is given to the ANFIS classifier to classify the heart disease and healthy patients. Sensitivity, Specificity and Mean Square Error (MSE) are calculated to evaluate the proposed MKL with ANFIS method. The proposed MKL with ANFIS is also compared with various existing deep learning methods such as Least Square with Support Vector Machine (LS with SVM), General Discriminant Analysis and Least Square Support Vector Machine (GDA with LS-SVM), Principal Component Analysis with Adaptive Neuro-Fuzzy Inference System (PCA with ANFIS) and Latent Dirichlet Allocation with Adaptive Neuro-Fuzzy Inference System (LDA with ANFIS). The results from the proposed MKL with ANFIS method has produced high sensitivity (98%), high specificity (99%) and less Mean Square Error (0.01) for the for the KEGG Metabolic Reaction Network dataset.     

支持向量机和最小二乘支持向量机的比较及应用研究

介绍和比较了支持向量机分类器和量小二乘支持向量机分类器的算法。并将支持向量机分类器和量小二乘支持向量机分类器应用于心脏病诊断，取得了较高的准确率。所用数据来自UCI bench—mark数据集。实验结果表明，支持向量机和量小二乘支持向量机在医疗诊断中有很大的应用潜力。     

冠心病介入治疗虚拟仿真操作训练系统的设计

冠心病的介入治疗效果明显、创伤小和恢复快,但在目前临床上专科医师模拟训练和医学专业实践教学中无实践对象,同时也容易受到辐射危害。现利用计算机等仿真技术开发一款冠心病介入治疗虚拟仿真操作训练系统,核心技术主要包括冠心病介入治疗设备基本单元、透明人体血管模型、无辐射仿DSA成像系统、三维动画学习软件等。操作者能够完全还原冠心病介入治疗中各种操作流程、技巧和手法,而且无X线辐射,安全可靠;创造性的利用自主研发的透明人体模型反复进行操作训练,经实验测试后可在医学生实践教学中直接得到推广运用,填补了本领域无实验设备和受试对象的空白。     

基于数学形态学及支持向量机的心率失常识别

为实现对不同类型的心电图自动分析,研究并提出了一种顺序筛选极大值的R波定位算法,并采用支持向量机(SVM)进行最后的心律失常心拍识别。定位算法以数学形态学为基础,结合心电图自身特点,定义R波筛选区间,避免了传统算法中的阈值选择;定位R波峰后以R波峰为中心提取不同类型的心率失常的心拍,选择径向基(RBF)支持向量机进行识别分类。使用MIT-BIH心率失常数据库文件进行实验仿真,结果表明,算法对含不同类型心拍的心电图R波峰正确检测率较高(99.36%),学习后的SVM能有效识别早搏、房颤、束支传导阻滞、正常等不用类型心拍,总体识别率达到99.75%。     

Identifying mortality risk factors amongst acute coronary syndrome patients admitted to Arabian Gulf hospitals using machine‐learning methods

Acute coronary syndrome (ACS) is a leading cause of mortality and morbidity in the Arabian Gulf. In this study, the in‐hospital mortality amongst patients admitted with ACS to Arabian Gulf hospitals is predicted using a comprehensive modelling framework that combines powerful machine‐learning methods such as support‐vector machine (SVM), Naïve Bayes (NB), artificial neural networks (NN), and decision trees (DT). The performance of the machine‐learning methods is compared with that of the performance of a commonly used statistical method, namely, logistic regression (LR). The study follows the current practise of computing mortality risk using risk scores such as the Global Registry of Acute Coronary Events (GRACE) score, which has not been validated for Arabian Gulf patients. Cardiac registry data of 7,000 patients from 65 hospitals located in Arabian Gulf countries are used for the study. This study is unique as it uses a contemporary data analytics framework. A k‐fold (k = 10) cross‐validation is utilized to generate training and validation samples from the GRACE dataset. The machine‐learning‐based predictive models often incur prejudgments for imbalanced training data patterns. To mitigate the data imbalance due to scarce observations for in‐hospital mortalities, we have utilized specialized methods such as random undersampling (RUS) and synthetic minority over sampling technique (SMOTE). A detailed simulation experimentation is carried out to build models with each of the five predictive methods (LR, NN, NB, SVM, and DT) for the each of the three datasets k‐fold subsamples generated. The predictive models are developed under three schemes of the k‐fold samples that include no data imbalance, RUS, and SMOTE. We have implemented an information fusion method rooted in computing weighted impact scores obtain for an individual medical history attributes from each of the predictive models simulated for a collective recommendation based on an impact score specific to a predictor. Finally, we grouped the predictors using fuzzy c‐mean clustering method into three categories, high‐, medium‐, and low‐risk factors for in‐hospital mortality due to ACS. Our study revealed that patients with medical history related to the presences of peripheral artery disease, congestive heart failure, cardiovascular transient ischemic attack valvular disease, and coronary artery bypass grafting amongst others have the most risk for in‐hospital mortality.     

Applying an ensemble convolutional neural network with Savitzky–Golay filter to construct a phonocardiogram prediction model

Coronary artery disease is a common chronic disease, also known as ischemic heart disease, which is a cardiac dysfunction caused by the insufficient blood supply to the heart and kills countless people every year. In recent years, coronary artery disease ranks first among the world’s top ten causes of death. Cardiac auscultation is still an important examination for diagnosing heart diseases. Many heart diseases can be diagnosed effectively by auscultation. However, cardiac auscultation relies on the subjective experience of physicians. To provide an objective diagnostic means and assist physicians in the diagnosis of heart sounds at a clinic, this study uses phonocardiograms to build an automatic classification model. This study proposes an automatic classification approach for phonocardiograms using deep learning and ensemble learning with a Savitzky–Golay filter. The experimental results showed that the proposed method is very competitive, and showed that the performance of the phonocardiogram classification model in hold out testing was 86.04% MAcc (86.46% sensitivity, 85.63% specificity), and in ten-fold cross validation it was 89.81% MAcc (91.73% sensitivity, 87.91% specificity). These two experimental results are all better than two state-of-art algorithms and show the potential to apply in real clinic situation.     

Classification of congestive heart failure with different New York Heart Association functional classes based on heart rate variability indices and machine learning

This study aims to evaluate the effect of heart rate variability (HRV) indices on the New York Heart Association (NYHA) classification of patients with congestive heart failure and to test the effectiveness of different machine learning algorithms. Twenty‐nine long‐term RR interval recordings from subjects (aged 34 to 79) with congestive heart failure (NYHA classes I, II, and III) in MIT‐BIH Database were studied. We firstly removed the unreasonable RR intervals and segment the RR recordings with a 300‐RR interval length window. Then the multiple HRV indexes were calculated for each RR segment. Support vector machine (SVM) and classification and regression tree (CART) methods were then separately used to distinguish patients with different NYHA classes based on the selected HRV indices. Receiver operating characteristic curve analysis was finally employed as the evaluation indicator to compare the performance of the two classifiers. The SVM classifier achieved accuracy, sensitivity, and specificity of 84.0%, 71.2%, and 83.4%, respectively, whereas the CART classifier achieved 81.4%, 66.5%, and 81.6%, respectively. The area under the curve of receiver operating characteristic for the two classifiers was 86.4% and 84.7%, respectively. It is possible for accurately classifying the NYHA functional classes I, II, and III when using the combination of HRV indices and machine learning algorithms. The SVM classifier performed better in classification than the CART classifier using the same HRV indices.