Table 3.
Confusion matrix
| Actual | Predicted | Row total | Overall class error | ||
|---|---|---|---|---|---|
| CN | MCI | Dementia | |||
| CN | 1218 (96.51%) | 44 (3.49%) | 0 (0.00%) | 1262 | 0.035 |
| MCI | 29 (1.16%) | 2382 (95.17%) | 92 (3.68%) | 2503 | 0.048 |
| Dementia | 0 (0.00%) | 107 (4.84%) | 2105 (95.16%) | 2212 | 0.048 |
| Column total | 1247 | 2533 | 2197 | 5977 | 0.046 |
This confusion matrix summarizes the performance of the random forest algorithm for classifying diagnoses based on contemporaneous observations. The table compares actual diagnoses observed in the training set with diagnoses predicted by the random forest based on observed continuous data and baseline predictors
CN control, MCI mild cognitive impairment