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Table 4 Variable selection from GA for prediction of conversion from HC to MCI/AD, compared with random selections of features.

From: Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease

# of features Average AUC (GA selected variable sets) Average AUC (randomly selected variable sets)
3 0.89 0.69
4 0.88 0.71
5 0.89 0.74
6 0.89 0.75
7 0.89 0.77
8 0.89 0.77
9 0.90 0.77
11 0.90 0.79
  1. The best performance is associated with sets comprising 3 to 11 features and can be classified as good-to excellent performance (AUC≈0.9). Random selection of features resulted in poor to borderline predictions (AUC<0.79).