A Biomedical Decision Support System Using LS-SVM Classifier with an Efficient and New Parameter Regularization Procedure for Diagnosis of Heart Valve Diseases

Çomak, Emre; Arslan, Ahmet

doi:10.1007/s10916-010-9500-5

Cited by 18 publications

(5 citation statements)

References 32 publications

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“…Digital stethoscopes improve murmur detection by converting sounds into electronic signals that can be further amplified, filtered and digitized [ 50 , 51 ] (Table 4 , Ref. [ 12 , 13 , 22 , 31 , 35 , 52 , 53 , 54 , 55 , 56 , 57 ]).…”

Section: Applications In Vhd Diagnosismentioning

confidence: 99%

Audiological Diagnosis of Valvular and Congenital Heart Diseases in the Era of Artificial Intelligence

Ainiwaer¹,

Kadier²,

Lian³

et al. 2023

Rev. Cardiovasc. Med.

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In recent years, electronic stethoscopes have been combined with artificial intelligence (AI) technology to digitally acquire heart sounds, intelligently identify valvular disease and congenital heart disease, and improve the accuracy of heart disease diagnosis. The research on AI-based intelligent stethoscopy technology mainly focuses on AI algorithms, and the commonly used methods are end-to-end deep learning algorithms and machine learning algorithms based on feature extraction, and the hot spot for future research is to establish a large standardized heart sound database and unify these algorithms for external validation; in addition, different electronic stethoscopes should also be extensively compared so that the algorithms can be compatible with different. In addition, there should be extensive comparison of different electronic stethoscopes so that the algorithms can be compatible with heart sounds collected by different stethoscopes; especially importantly, the deployment of algorithms in the cloud is a major trend in the future development of artificial intelligence. Finally, the research of artificial intelligence based on heart sounds is still in the preliminary stage, although there is great progress in identifying valve disease and congenital heart disease, they are all in the research of algorithm for disease diagnosis, and there is little research on disease severity, remote monitoring, prognosis, etc., which will be a hot spot for future research.

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Section: Applications In Vhd Diagnosismentioning

confidence: 99%

Audiological Diagnosis of Valvular and Congenital Heart Diseases in the Era of Artificial Intelligence

Ainiwaer¹,

Kadier²,

Lian³

et al. 2023

Rev. Cardiovasc. Med.

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“…As a simplification, Rubio et al [22] proposed a modified version of SVM called least square support vector machine (LS-SVM) which resulted in a set of linear equations instead of a quadratic program. LS-SVM has been applied to prediction and classification with promising results, as can be seen in some works [23][24][25][26].…”

Section: Introductionmentioning

confidence: 98%

Forecasting Models for Hydropower Unit Stability Using LS-SVM

Qiao

Chen

2015

Mathematical Problems in Engineering

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This paper discusses a least square support vector machine (LS-SVM) approach for forecasting stability parameters of Francis turbine unit. To achieve training and testing data for the models, four field tests were presented, especially for the vibration inY-direction of lower generator bearing (LGB) and pressure in draft tube (DT). A heuristic method such as a neural network using Backpropagation (NNBP) is introduced as a comparison model to examine the feasibility of forecasting performance. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to the NNBP, which is of significant importance to better monitor the unit safety and potential faults diagnosis.

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“…There is extensive literature on the applications of the Renyi's entropy in many¯elds from biology, medicine and genetics. 23,24 Further Renyi's entropybased proposed algorithm performs well on datasets of nonspherical shape and capable of clustering a high-dimensional dataset. However, the random selection of initial prototypes of fuzzy C-means-based algorithms lead more number of iterations to reach the termination criterion, 25,26 therefore in order to avoid irrelevant initial random prototypes this paper introduces a prototype initialization method.…”

Section: Introductionmentioning

confidence: 98%

Fuzzy C-Means in Finding Subtypes of Cancers in Cancer Database

Kannan

Ramthilagam²,

Devi

et al. 2014

J. Innov. Opt. Health Sci.

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Finding subtypes of cancer in breast cancer database is an extremely di±cult task because of heavy noise by measurement error. Most of the recent clustering techniques for breast cancer database to achieve cancerous and noncancerous often weigh down the interpretability of the structure. Hence, this paper tries to¯nd e®ective Fuzzy C-Means-based clustering techniques to identify the proper subtypes of cancer in breast cancer database. This paper obtains the objective function of e®ective Fuzzy C-Means clustering techniques by incorporating the kernel induced distance function, Renyi's entropy function, weighted distance measure and neighborhood termsbased spatial context. The e®ectiveness of the proposed methods are proved through the experimental works on Lung cancer database, IRIS dataset, Wine dataset, Checkerboard dataset, Time Series dataset and Yeast dataset. Finally, the proposed methods are implemented successfully to cluster the breast cancer database into cancerous and noncancerous. The clustering accuracy has been validated through error matrix and silhouette method.

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A Biomedical Decision Support System Using LS-SVM Classifier with an Efficient and New Parameter Regularization Procedure for Diagnosis of Heart Valve Diseases

Cited by 18 publications

References 32 publications

Audiological Diagnosis of Valvular and Congenital Heart Diseases in the Era of Artificial Intelligence

Audiological Diagnosis of Valvular and Congenital Heart Diseases in the Era of Artificial Intelligence

Forecasting Models for Hydropower Unit Stability Using LS-SVM

Fuzzy C-Means in Finding Subtypes of Cancers in Cancer Database

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