Table 8 Mean scores on the Agincourt dataset using the WHO categories
From: Automatically determining cause of death from verbal autopsy narratives
Precision | Sensitivity | F 1 | PCCC | CSMFA | CCCSMFA | |
|---|---|---|---|---|---|---|
Adult (15–69 years) | ||||||
Naïve Bayes | .433 | .448 | .431 | .432 | .876 | .662 |
Random forest | .438 | .464 | .436 | .448 | .832 | .543 |
SVM | .502 | .505 | .491 | .490 | .857 | .612 |
Neural network | .470 | .495 | .451 | .480 | .750 | .322 |
Child (29 days–14 years) | ||||||
Naïve Bayes | .378 | .388 | .370 | .360 | .793 | .437 |
Random forest | .456 | .450 | .431 | .425 | .799 | .453 |
SVM | .471 | .465 | .452 | .440 | .816 | .499 |
Neural network | .388 | .428 | .374 | .402 | .667 | .095 |
Neonate (<29 days) | ||||||
Naïve Bayes | .276 | .384 | .305 | .296 | .610 | -.060 |
Random forest | .292 | .369 | .314 | .279 | .673 | .111 |
SVM | .391 | .405 | .373 | .320 | .733 | .274 |
Neural network | .156 | .265 | .179 | .160 | .502 | -.353 |