Model performance evaluation
In this study, we evaluate model performance in two ways. First, check if the mini network disorder probability can be used as a proxy for the disease progression by performing regression analysis of mini network and MMSE.
Second, check if the model can improve the accuracy of AD diagnosis by comparing classification accuracy of AD vs. normal and MCI vs. normal. Two Support vector machines (SVM) are trained for measure the classification performance: SVM based on mini network disruption parameters (U, K, φ) and mini network disorder probability and SVM based on CSF markers (tau, P-tau, and Aβ). The classification performance is evaluated by 10-fold cross-validation.