Sample ID | Class | Classifier | Score HS_Polyp_Y | Score HS_Polyp_N | Predicted | Call |

A. Subjects where both models call FN | ||||||

HS_23 | HS_Polyp_Y | Naïve Bayes | 0.007 | 0.993 | HS_Polyp_N | FN |

Neural Network | 0 | 1 | HS_Polyp_N | FN | ||

HS_341 | HS_Polyp_Y | Naïve Bayes | 0.159 | 0.841 | HS_Polyp_N | FN |

Neural Network | 0.177 | 0.823 | HS_Polyp_N | FN | ||

HS_372 | HS_Polyp_Y | Naïve Bayes | 0.213 | 0.787 | HS_Polyp_N | FN |

Neural Network | 0.208 | 0.792 | HS_Polyp_N | FN | ||

HS_381 | HS_Polyp_Y | Naïve Bayes | 0.005 | 0.995 | HS_Polyp_N | FN |

Neural Network | 0.373 | 0.627 | HS_Polyp_N | FN | ||

HS_384 | HS_Polyp_Y | Naïve Bayes | 0.026 | 0.974 | HS_Polyp_N | FN |

Neural Network | 0.256 | 0.744 | HS_Polyp_N | FN | ||

HS_386 | HS_Polyp_Y | Naïve Bayes | 0.35 | 0.65 | HS_Polyp_N | FN |

Neural Network | 0.418 | 0.582 | HS_Polyp_N | FN | ||

HS_413 | HS_Polyp_Y | Naïve Bayes | 0.328 | 0.672 | HS_Polyp_N | FN |

Neural Network | 0.427 | 0.573 | HS_Polyp_N | FN | ||

HS_423 | HS_Polyp_Y | Naïve Bayes | 0.01 | 0.99 | HS_Polyp_N | FN |

Neural Network | 0.002 | 0.998 | HS_Polyp_N | FN | ||

HS_461 | HS_Polyp_Y | Naïve Bayes | 0.578 | 0.422 | HS_Polyp_N | FN |

Neural Network | 0.303 | 0.697 | HS_Polyp_N | FN | ||

B. Subjects where one model calls FN and the other calls TP | ||||||

HS_363 | HS_Polyp_Y | Naïve Bayes | 0.779 | 0.221 | HS_Polyp_Y | TP |

Neural Network | 0.034 | 0.966 | HS_Polyp_N | FN | ||

HS_367 | HS_Polyp_Y | Naïve Bayes | 0.905 | 0.095 | HS_Polyp_Y | TP |

Neural Network | 0.365 | 0.635 | HS_Polyp_N | FN | ||

HS_373 | HS_Polyp_Y | Naïve Bayes | 0.578 | 0.422 | HS_Polyp_N | FN |

Neural Network | 0.992 | 0.008 | HS_Polyp_Y | TP | ||

HS_403 | HS_Polyp_Y | Naïve Bayes | 0.627 | 0.373 | HS_Polyp_N | FN |

Neural Network | 0.982 | 0.018 | HS_Polyp_Y | TP | ||

HS_407 | HS_Polyp_Y | Naïve Bayes | 0.076 | 0.924 | HS_Polyp_N | FN |

Neural Network | 0.991 | 0.009 | HS_Polyp_Y | TP | ||

HS_412 | HS_Polyp_Y | Naïve Bayes | 0.317 | 0.683 | HS_Polyp_N | FN |

Neural Network | 0.528 | 0.472 | HS_Polyp_Y | TP | ||

HS_417 | HS_Polyp_Y | Naïve Bayes | 0.894 | 0.106 | HS_Polyp_Y | TP |

Neural Network | 0.03 | 0.97 | HS_Polyp_N | FN | ||

HS_420 | HS_Polyp_Y | Naïve Bayes | 0.524 | 0.476 | HS_Polyp_N | FN |

Neural Network | 0.654 | 0.346 | HS_Polyp_Y | TP | ||

HS_427 | HS_Polyp_Y | Naïve Bayes | 0.084 | 0.916 | HS_Polyp_N | FN |

Neural Network | 0.536 | 0.464 | HS_Polyp_Y | TP | ||

HS_45 | HS_Polyp_Y | Naïve Bayes | 0.651 | 0.349 | HS_Polyp_Y | TP |

Neural Network | 0.079 | 0.921 | HS_Polyp_N | FN | ||

HS_507 | HS_Polyp_Y | Naïve Bayes | 0.711 | 0.289 | HS_Polyp_N | FN |

Neural Network | 0.682 | 0.318 | HS_Polyp_Y | TP | ||

HS_6 | HS_Polyp_Y | Naïve Bayes | 0.031 | 0.969 | HS_Polyp_N | FN |

Neural Network | 0.731 | 0.269 | HS_Polyp_Y | TP | ||

HS_62 | HS_Polyp_Y | Naïve Bayes | 0.686 | 0.314 | HS_Polyp_Y | TP |

Neural Network | 0.453 | 0.547 | HS_Polyp_N | FN |

Classification scores are presented for each model and subject where the true class (ie, Polyp-Y or Polyp-N) is tabulated along with the model scores and predictions.

FN, false negative;HS, home collected stool sample;Polyp_N, polyp-negative group;Polyp_Y, polyp-positive group;TP, true positive.