Table 3 Best performing DNN architectures.
From: A deep neural network approach to predicting clinical outcomes of neuroblastoma patients
Configuration | Architecture | Balanced accuracy |
|---|---|---|
Clinical outcome = ‘Death from disease’ | ||
Fischer-M, centralities | [8,8,8,2] | 87.3% |
Fischer-M, modularities | [8,4] | 83.9% |
Fischer-M, both | [8,8,8] | 86.8% |
Fischer-R, centralities | [8,8,8,4] | 85.8% |
Fischer-R, modularities | [8,8,8,2] | 82.1% |
Fischer-R, both | [2,2,2,2] | 85.2% |
Fischera, centralities | [8,2,2] | 86.1% |
Fischera, modularities | [8,2,2] | 84.7% |
Fischera, both | [8,8,4] | 84.7% |
Clinical outcome = ‘Disease progression’ | ||
Fischer-M, centralities | [8,8,8,2] | 84.3% |
Fischer-M, modularities | [8,8,2] | 82.3% |
Fischer-M, both | [4,4,2] | 83.7% |
Fischer-R, centralities | [8,8,4] | 83.7% |
Fischer-R, modularities | [8,2,2] | 79.1% |
Fischer-R, both | [8,8,8,8] | 77.9% |
Fischera, centralities | [4,2,2,2] | 84.7% |
Fischera, modularities | [8,8] | 79.6% |
Fischera, both | [4,2] | 81.5% |