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Table 4 Prediction models assessed with Neural Network approach

From: Prediction of steroid resistance and steroid dependence in nephrotic syndrome children

Prediction parameter Neural Network
Train a Test b
NS prediction Sensitivity % 75% (75/100) 38% (9/24)
NS prediction Specificity % 80% (35/44) 91% (10/11)
Total number of correct calls % 76% (110/144) 54% (19/35)
Log Loss 0.483 0.682
AUC 0.825 0.561
SR prediction Sensitivity % 68% (34/50) 33% (1/3)
SR prediction Specificity % 98% (49/50) 100% (21/21)
Total number of correct calls % 83% (83/100) 92% (22/24)
Log Loss 0.391 0.72
AUC 0.932 0.778
SD prediction Sensitivity % 62% (16/26) 80% (4/5)
SD prediction Specificity % 91% (32/35) 80% (4/5)
Total number of correct calls % 79% (48/61) 80% (8/10)
Log Loss 0.704 1.208
AUC 0.866 0.72
SR prediction Sensitivity % 82% (28/34) 63% (12/19)
SD prediction Sensitivity % 46% (13/28) 67% (2/3)
PSS prediction Sensitivity % 63% (24/38) 50% (1/2)
Total number of correct calls % 65% (65/100) 63% (15/24)
Log Loss 0.756 0.761
  1. NS nephrotic syndrome, SR steroid resistant, SS steroid sensitive, SD steroid dependent, PSS primarily steroid sensitive, AUC area under the curve
  2. Each model was run using aTrain set, comprising 80% of data, and bTest set, comprising the rest 20% of data