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linear discriminant analysis model  (MathWorks Inc)


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    MathWorks Inc linear discriminant analysis model
    Analysis of receiver operating characteristic curves for different classification models. *, P<0.05. SVM, support vector machine; AUC, area under the curve; CI, confidence interval; KNN, <t>K-nearest</t> <t>neighbor;</t> LR, logistic regression; <t>LDA,</t> linear discriminant analysis.
    Linear Discriminant Analysis Model, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/linear+discriminant+analysis+model/pmc11485364-70-9-26
    Average 90 stars, based on 1 article reviews
    linear discriminant analysis model - by Bioz Stars, 2026-09
    90/100 stars

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    1) Product Images from "Support vector machine classification of irritable bowel syndrome patients based on whole-brain resting-state functional connectivity features"

    Article Title: Support vector machine classification of irritable bowel syndrome patients based on whole-brain resting-state functional connectivity features

    Journal: Quantitative Imaging in Medicine and Surgery

    doi: 10.21037/qims-24-892

    Analysis of receiver operating characteristic curves for different classification models. *, P<0.05. SVM, support vector machine; AUC, area under the curve; CI, confidence interval; KNN, K-nearest neighbor; LR, logistic regression; LDA, linear discriminant analysis.
    Figure Legend Snippet: Analysis of receiver operating characteristic curves for different classification models. *, P<0.05. SVM, support vector machine; AUC, area under the curve; CI, confidence interval; KNN, K-nearest neighbor; LR, logistic regression; LDA, linear discriminant analysis.

    Techniques Used: Plasmid Preparation

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    Article Title: Characterizing vocal tremor in progressive neurological diseases via automated acoustic analyses.
    Article Snippet: This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record.. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article.. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

    Article Title: Support vector machine classification of irritable bowel syndrome patients based on whole-brain resting-state functional connectivity features
    Article Snippet: In this study, machine learning models based on linear discriminant analysis (LDA), logistic regression (LR), K-nearest neighbor (KNN), and support vector machine (SVM) models accomplished by MATLAB were used to classify the IBS patients and HCs.

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    Analysis of receiver operating characteristic curves for different classification models. *, P<0.05. SVM, support vector machine; AUC, area under the curve; CI, confidence interval; KNN, K-nearest neighbor; LR, logistic regression; LDA, linear discriminant analysis.

    Journal: Quantitative Imaging in Medicine and Surgery

    Article Title: Support vector machine classification of irritable bowel syndrome patients based on whole-brain resting-state functional connectivity features

    doi: 10.21037/qims-24-892

    Figure Lengend Snippet: Analysis of receiver operating characteristic curves for different classification models. *, P<0.05. SVM, support vector machine; AUC, area under the curve; CI, confidence interval; KNN, K-nearest neighbor; LR, logistic regression; LDA, linear discriminant analysis.

    Article Snippet: In this study, machine learning models based on linear discriminant analysis (LDA), logistic regression (LR), K-nearest neighbor (KNN), and support vector machine (SVM) models accomplished by MATLAB were used to classify the IBS patients and HCs.

    Techniques: Plasmid Preparation