Table 5. Hypothesis testing over accuracy with different input datasets.

Comparisons	E+M vs. E		E+M vs. M		E+M vs. META-22+M
Methods	Mean %	p-Value	Mean %	p-Value	Mean %	p-Value
wHLFS	0.29(0.53)	0.2905	1.72(0.76)	0.0124	1.28(0.64)	0.0238
wHLFS+SVM	1.8(0.71)	0.0064	5.81(0.76)	<0.0001	5.95(0.73)	<0.0001
wHLFS+RF	1.69(0.70)	0.0080	4.34(0.61)	<0.0001	3.77(0.63)	<0.0001

MCI converter/non-converter classification comparison with different datasets in terms of accuracy. Methods applied here include the combinations of wHLFS and different classification methods. The different feature datasets are META (E), MRI (M), and META without baseline cognitive scores (META-22). With the same input training and testing samples and the same method with the same parameters, we compare the performances based on different input feature datasets. By varying the sets of training samples and testing samples and the settings of parameters, we obtain a series of comparisons. Then paired t-tests are performed on the performance by using E+M dataset and the performance by using another dataset. A positive mean value means the average improvement on accuracy by using E+M dataset. A p-value less than 0.05 means using E+M dataset can achieve a significant improvement on accuracy. The standard deviations of mean values are shown in the parentheses.