Machine learning algorithms to study the hospitalization after cesarean section: a multicenter analysis

Caesarean section (CS) is a surgical procedure in which the child is given birth through a cut in the mother's abdomen. The surgery is relatively safe for both, especially when planned, even if it is still a surgical operation with all the necessary risks. In all developed countries and in particular in Italy now the Caesarean section is very widespread, in fact statistics estimate an unstoppable and disproportionate increase. The increase in the rate of CS, increases the stay in the hospital, as it is a real intervention and as a result there is an increase in operating costs. For this reason, it is interesting and important to analyze the Length of Stay (LOS) and to implement models that allow to predict such indicator. The objective of this investigation is to examine the Length-of-Stay (LOS) among all patients who undergo cesarean section (CS) at the University Hospital "Federico II"in Naples (Italy). In the work, prediction models of LOS were created with a multiple linear regression analysis and machine learning.