| Parameters | Training-Validation-Testing (70% - 15% - 15%) | Training-Validation-Testing (50% - 25% - 25%) | ||||
| SCG | LM | RPROP | SCG | LM | RPROP | |
| Number of Neurons (Input) | 6 | 6 | 6 | 6 | 6 | 6 |
| Number of Neurons (Hidden) | 10 | 10 | 10 | 10 | 10 | 10 |
| Number of Neurons (Output) | 2 | 2 | 2 | 2 | 2 | 2 |
| Learning Coefficient | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 | 0.75 |
| % of Error | 3.60% | 5.40% | 3.60% | 3.00% | 1.80% | 1.80% |
| Number of Epochs | 13 | 8 | 9 | 13 | 12 | 60 |
| Training* | SCG | LM | NRP | SCG | LM | NRP |
| Gradient | 0.022 | 0.003 | 0.424 | 0.029 | 0.0008 | 6.9e−6 |
| Training Optimization Time (s) | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
| Number of Training Dataset (Fall-Non Fall) | 117 (35 - 82) | 117 (35 - 82) | 117 (35 - 82) | 83 (23 - 60) | 83 (23 - 60) | 83 (23 - 60) |
| Number of Validation Dataset (Fall-Non Fall) | 25 (7 - 18) | 25 (7 - 18) | 25 (7 - 18) | 42 (16 - 26) | 42 (16 - 26) | 42 (16 - 26) |
| Number of Test Dataset (Fall-Non Fall) | 25 (9 - 16) | 25 (9 - 16) | 25 (9 - 16) | 42 (12 - 30) | 42 (12 - 30) | 42 (12 - 30) |
| Number of Misclassification (Training) | 2 | 5 | 4 | 2 | 0 | 0 |
| Training Dataset CDR | 98.30% | 95.70% | 96.60% | 97.60% | 100.00% | 100.00% |
| Number of Misclassification (Validation) | 1 | 1 | 1 | 0 | 0 | 0 |
| Validation Dataset CDR | 96.00% | 96.00% | 96.00% | 100.00% | 100.00% | 100.00% |
| Number of Misclassification (Test) | 3 | 3 | 1 | 3 | 3 | 3 |
| Test Dataset CDR | 88.00% | 88.00% | 96.00% | 92.90% | 92.90% | 92.90% |
| Overall CDR | 96.40% | 94.60% | 96.40% | 97.00% | 98.20% | 98.20% |
| Specificity | 98.3% | 97.4% | 98.3% | 97.4% | 97.4% | 97.4% |
| Sensitivity | 92.2% | 88.2% | 92.2% | 96.1% | 100.0% | 100.0% |