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Conference on IT in Business, Industry and Government (CSIBIG)

作者： Sonawane, Jayshril S. Patil, D. R. SESs RC Patel Inst Technol Shirpur Dept Comp Engn Shirpur Maharashtra India

ISBN: (纸本)9781479930630;9781479930647

In medical field the disease diagnosis is often made based on the knowledge and experience of the medical practitioner. Due to this there are chances of errors, unwanted biases and also takes longer time in accurate diagnosis of disease. In case of heart disease, its diagnosis is most difficult task. It depends on the careful analysis of different clinical and pathological data of the patient by medical experts, which is complicated process. Due to advancement in machine learning, computer and information technology, the researchers and medical practitioners in large extent are interested in the development of automated system for the prediction of heart disease. In this paper we present a prediction system for heart disease using learning vector quantization neural network algorithm The neural network in this system accepts 13 clinical features as input and predicts that there is a presence or absence of heart disease in the patient, along with different performance measures.

关键词： heart disease machine learning learning vector quantization algorithm

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A New generalized learning vector quantization algorithm

A New generalized learning vector quantization algorithm

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2nd International Conference on Image and Graphics

作者： Zhou, SS Zhou, LH Liu, WG Xidian Univ Multimedia Technol Inst Xian 710071 Peoples R China

ISBN: (纸本)0819446564

The disadvantage of the generalized learning vector quantization (GLVQ) and fuzzy generalization learning vector quantization (FGLVQ) algorithms is discussed. A revised GLVQ (RGLVQ) algorithm is proposed. Because the iterative coefficients of the proposed algorithms are properly bounded, the performance of our algorithms is invariant under uniform scaling of the entire data set unlike Pal's GLVQ, and the initial learning rate is not sensitive to the number of prototypes as Karayiannis's FGLVQ. The proposed algorithms are tested and evaluated using the IRIS data set. The efficiency of the proposed algorithms is also illustrated by their use in codebook design required for image compression based on vector quantization. The training time of RGLVQ algorithm is reduced by 20% as compared with Karayiannis's FGLVQ but the performance is similar.

关键词： learning vector quantization algorithm competitive network image compression

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Neural Network algorithm Based on LVQ for Myocardial Infarction Detection and Localization Using Multi-Lead ECG Data

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Computers, Materials & Continua 2025年第3期82卷 5257-5284页

作者： Kassymbek Ozhikenov Zhadyra Alimbayeva Chingiz Alimbayev Aiman Ozhikenova Yeldos Altay Department of Robotics and Engineering Tools of Automation Satbayev UniversityAlmaty050010Kazakhstan Joldasbekov Institute of Mechanics and Engineering Almaty050010Kazakhstan

Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient *** the available functional diagnostic methods,electrocardiography(ECG)is particularly well-known for its ability to detect ***,confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in *** study,therefore,proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of *** particular,the learning vector quantization(LVQ)algorithm was applied,considering the contribution of each ECG lead in the 12-channel system,which obtained an accuracy of 87%in localizing damaged *** developed model was tested on verified data from the PTB database,including 445 ECG recordings from both healthy individuals and MI-diagnosed *** results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients,serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage.A comprehensive comparison was performed,including CNN,SVM,and Logistic Regression,to evaluate the proposed LVQ *** results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency,making it suitable for resource-constrained *** study also applies a carefully designed data pre-processing flow,including class balancing and noise removal,which improves the reliability and reproducibility of the *** aspects highlight the potential application of the LVQ model in cardiac diagnostics,opening up prospects for its use along with more complex neural network architectures.

关键词： Electrocardiography 12-lead electrocardiogram myocardial infarction heart disease learning vector quantization algorithm machine learning

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