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Table 8 P values of each variable in the different models.

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

Conversion from HC to MCI/AD

Conversion from MCI to AD

Variables

Pvalues_m1G

Pvalues_m1S

Pvalues_m2G

Pvalues _m2S

Variables

Pvalues _m3G

Pvalues _m3S

Pvalues _m4G

Pvalues _m4S

V1

0.07

 

0.002

 

V1

  

0.170

 

V3

<0.001

<0.001

<0.001

<0.001

V10

0.011

0.019

0.056

0.011

V5

0.003

<0.001

0.014

<0.001

V15

  

0.048

 

V7

  

0.004

 

V16

 

0.046

 

0.117

V8

 

0.01

0.251

0.012

V18

   

0.245

V9

   

0.021

V19

0.009

0.006

0.008

0.026

V13

  

0.139

 

V24

0.043

 

0.034

0.124

V16

  

0.109

0.087

V27

 

0.284

 

0.233

V17

  

0.004

0.020

V28

   

0.092

V18

0.007

0.003

0.001

0.004

V31

0.142

 

0.154

 

V25

   

0.828

V32

  

0.549

 

V33

   

0.063

V35

0.104

0.014

0.181

0.033

  1. The results provide statistical support for our finding that variables 3, 5 and 18 dominate prediction of progression of HC to MCI/AD. The prediction of progression from MCI to AD consistently showed the importance of variable 19, and also of variables 10 and 35. These results indicate that for conversion of MCI to AD, the combinations of variables are more important than the contribution of individual variables.
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BMC Bioinformatics

ISSN: 1471-2105

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