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Confusion matrix for the predicting condition of epileptic patients (classification-tree analysis) based on the <t> SIFT-MS spectra. </t> “Sensitivity” is defined as the percentage of seizure-related spectra that are well predicted by the model, whereas “specificity” is defined as the percentage of non-seizure-related spectra that are correctly rejected.
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Confusion matrix for the predicting condition of epileptic patients (classification-tree analysis) based on the SIFT-MS spectra. “Sensitivity” is defined as the percentage of seizure-related spectra that are well predicted by the model, whereas “specificity” is defined as the percentage of non-seizure-related spectra that are correctly rejected.

Journal: Scientific Reports

Article Title: Prediction and detection of human epileptic seizures based on SIFT-MS chemometric data

doi: 10.1038/s41598-020-75478-8

Figure Lengend Snippet: Confusion matrix for the predicting condition of epileptic patients (classification-tree analysis) based on the SIFT-MS spectra. “Sensitivity” is defined as the percentage of seizure-related spectra that are well predicted by the model, whereas “specificity” is defined as the percentage of non-seizure-related spectra that are correctly rejected.

Article Snippet: This predictive model was developed from the 1888 variables of the SIFT-MS spectra by using Minitab version 19.2020.1. (Minitab Inc. PA, USA).

Techniques: