Support Vector Machine Ensembles for Intelligent Diagnosis of Valvular Heart Disease

2011 ◽  
Vol 36 (4) ◽  
pp. 2649-2655 ◽  
Author(s):  
Abdulkadir Sengur
Keyword(s):  
Support Vector Machine ◽  
Heart Disease ◽  
Valvular Heart Disease ◽  
Support Vector ◽  
Intelligent Diagnosis

A Novel Image Segmentation Method for Cardiac MRI Using Support Vector Machine Algorithm Based on Particle Swarm Optimization

2021 ◽  
Vol 11 (12) ◽  
pp. 3174-3180
Author(s):  
Guanghui Wang ◽  
Lihong Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Heart Disease ◽  
Cardiac Mri ◽  
Support Vector ◽  
Swarm Optimization ◽  
Running Time ◽  
Segmentation Accuracy

At present, heart disease not only has a significant impact on the quality of human life but also poses a greater impact on people’s health. Therefore, it is very important to be able to diagnose heart disease as early as possible and give corresponding treatment. Heart image segmentation is the primary operation of intelligent heart disease diagnosis. The quality of segmentation directly determines the effect of intelligent diagnosis. Because the running time of image segmentation is often longer, coupled with the characteristics of cardiac MR imaging technology and the structural characteristics of the cardiac target itself, the rapid segmentation of cardiac MRI images still has challenges. Aiming at the long running time of traditional methods and low segmentation accuracy, a medical image segmentation (MIS) method based on particle swarm optimization (PSO) optimized support vector machine (SVM) is proposed, referred to as PSO-SVM. First, the current iteration number and population number in PSO are added to the control strategy of inertial weight λ to improve the performance of PSO inertial weight λ. Find the optimal penalty coefficient C and γ in the gaussian kernel function by PSO. Then use the SVM method to establish the best classification model and test the data. Compared with traditional methods, this method not only shortens the running time, but also improves the segmentation accuracy. At the same time, comparing the influence of traditional inertial weights on segmentation results, the improved method reduces the average convergence algebra and shortens the optimization time.

Classification of Heart Disease Using Support Vector Machine

2019 ◽  
Vol 16 (5) ◽  
pp. 2623-2627
Author(s):  
V Kirankumar ◽  
Somula Ramasubbareddy ◽  
G Kannayaram ◽  
K Nikhil Kumar
Keyword(s):  
Support Vector Machine ◽  
Heart Disease ◽  
Support Vector

Mill Gear Box of Intelligent Diagnosis Based on Support Vector Machine Parameters Optimization

2014 ◽  
Vol 697 ◽  
pp. 239-243 ◽  
Author(s):  
Xiao Hui Liu ◽  
Yong Gang Xu ◽  
De Ying Guo ◽  
Fei Liu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Rolling Mill ◽  
Optimal Parameter ◽  
Structural Parameters ◽  
Parameters Optimization ◽  
Support Vector ◽  
Intelligent Diagnosis ◽  
Gear Box ◽  
Parameter Values

For mill gearbox fault detection problems, and puts forward combining support vector machine (SVM) and genetic algorithm, is applied to rolling mill gear box fault intelligent diagnosis methods. The choice of parameters of support vector machine (SVM) is a very important for the SVM performance evaluation factors. For the selection of structural parameters of support vector machine (SVM) with no theoretical support, select and difficult cases, in order to reduce the SVM in this respect, puts forward the genetic algorithm to optimize parameters, and the algorithm of the model is applied to rolling mill gear box in intelligent diagnosis, using the global searching property of genetic algorithm and support vector machine (SVM) of the optimal parameter values. Results showed that the suitable avoided into local solution optimization, the method to improve the diagnostic accuracy and is a very effective method of parameter optimization, and intelligent diagnosis for rolling mill gear box provides an effective method.

scholarly journals Feature Selection using Hybrid Dragonfly Algorithm in a Heart Disease Predication System

2019 ◽  
Vol 8 (6) ◽  
pp. 2862-2867
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Heart Disease ◽  
Mutual Information ◽  
Multilayer Perceptron ◽  
Time Algorithm ◽  
Support Vector ◽  
Ordinary People ◽  
Fatal Disease ◽  
Dragonfly Algorithm

The heart disease considers as one of the fatal disease in many countries. The main reason is due to the approved methods of diagnostic are not available to the ordinary people. Many studies have been done to handle this case with the use of both methods of soft computing and machine learning. In this study, a hybrid binary dragonfly algorithm and mutual information proposed for feature selection, support vector machine and multilayer perceptron employed for classification. The Statlog dataset used for experiments. Out of a total of 270 instances of patient data, 216 employees for the purpose of practicing, 54 of them used for the purpose of examining. Maximum classification accuracy of 94.44% achieved with support vector machine and 92.59% with multilayer perceptron on features selected with binary dragonfly algorithm, whereas with features obtained from mutual information combined with binary dragonfly (MI_BDA) algorithm support vector machine and multilayer perceptron attained an accuracy of 96.29%. The time algorithm takes reduced from 15.4 with binary dragonfly algorithm to 6.95 seconds with MI_BDA.

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