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

<div>The XGBoost classifier using (a) DC datasets, (b) fALFF datasets, (c) mPerAF datasets, (d) PerAF datasets, and (e) Wavelet-ALFF datasets based on the SHAP algorithm.</div>

Computational Intelligence and Neuroscience

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Figure 2

Figure 2: Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning 

Figure 2 | Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning 