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Table 1 Loading matrix after quartimin rotation of MCA data.

From: Multivariate modeling to identify patterns in clinical data: the example of chest pain

  F1 F2 F3 F4 F5 F6
Patient does not think that chest pain results from heart disease 0.39      
Chest pain at the moment of consultation 0.50      
Pain dependent on respiration 1.11      
Pain dependent on stress 0.82    1.20   
Pain dependent on exercise 0.87      
Localisation right 0.54      
No pain at palpation 0.45      
Pain more than once per day 0.57      
Patient is different than usual   1.96     
Something wrong with my patient.   2.37     
Patient is pale   2.21     
Patient is anxious   0.82     
Patient is cold sweated   3.42     
Patient is too quiet   1.83     
Patient is reddened   1.20     
Patient is excited   1.01     
Patient is short of breath   1.88     
Acute pain ≤48 hours   0.82     
Known heart failure   0.79     
Male gender    0.57    
Emesis    0.51    
No diabetes    -1.31    
No hypertension    -0.79    
No heart failure    -1.24    
No overweight    -0.76    
No lack of exercise    -0.96    
Radiation of pain into epigastrum    0.52    
Pressing pain     0.61   
Respiratory distress     0.84   
Tightness of the chest     0.92   
Radiation into left arm     0.64   
Duration under 30 minutes     0.39   
Patient is not anxious      -0.86  
Hollow pain      0.60  
Cough      0.90  
Respiratory infection      1.08  
Less frequent pain      0.49  
Duration of pain less than 1 minute       0.56
Stinging pain       0.33
  1. Depicted are the highest interpretable positive and negative loadings per factor.