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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