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Table 6 Variable selection from GA for prediction of conversion from MCI to 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)
4 0.86 0.67
5 0.85 0.67
6 0.85 0.68
7 0.86 0.69
8 0.86 0.69
  1. The best performance is associated with the feature sets of lengths 7 and 8, that can be classified as good performance (0.9>AUC>0.8). Random selection of features resulted in poor prediction (AUC<0.7).