Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning

<div>ROC curves of XGBoost classifier trained on 90% of datasets and tested on the remaining 10% of datasets. ROC curves of XGBoost classifier on (a) DC datasets, (b) fALFF datasets, (c) mPerAF datasets, (d) PerAF datasets, and (e) Wavelet-ALFF datasets.</div>

Computational Intelligence and Neuroscience

fig1

Figure 1

Figure 1: Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning 

Figure 1 | Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning 