Machine Learning for inconsistency detection in hospital cesarean bills
Aims: Evaluate the most adequate Machine Learning models to the analysis of inconsistencies and irregularities on the final values on the bills presented to the health care plan operator. Methods: 1,602 medical bills’ receipts regarding caesarean hospitalizations, 50.20% of the receipts being inconsistent and 49.80% of the receipts not presenting inconsistencies. The selected documents are from the period between 2015 a 2019.