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. 2025 Jan 20;25(1):238.
doi: 10.1186/s12889-025-21530-z.

Use of artificial intelligence to study the hospitalization of women undergoing caesarean section

Arianna Scala  1 , Giuseppe Bifulco  2 , Anna Borrelli  3 , Rosanna Egidio  3 , Maria Triassi  2   4 , Giovanni Improta  2   4
Affiliations

Use of artificial intelligence to study the hospitalization of women undergoing caesarean section

Arianna Scala et al. BMC Public Health. .

Abstract

Objective: The incidence of caesarean sections (CSs) has increased significantly in recent years, especially in developed countries. This study aimed to identify the factors that most influence the length of hospital stay (LOS) after a CS, using data from 9,900 women who underwent CS at the "Federico II" University Hospital of Naples between 2014 and 2021.

Methods: Various artificial intelligence models were employed to analyze the relationships between the LOS and a set of independent variables, including maternal and foetal characteristics. The analysis focused on identifying the model with the best predictive performance and specific comorbidities impacting LOS.

Results: A multiple linear regression model determined the highest R-value (0.815), indicating a strong correlation between the identified variables and LOS. Significant predictors of LOS included abnormal foetuses, cardiovascular disease, respiratory disorders, hypertension, haemorrhage, multiple births, preeclampsia, previous delivery complications, surgical complications, and preoperative LOS. In terms of classification models, the decision tree yielded the highest accuracy (75%).

Conclusions: The study concluded that certain comorbidities, such as cardiovascular disease and preeclampsia, significantly impact LOS following a CS. These findings can assist hospital management in optimizing resource allocation and reducing costs by focusing on the most influential factors.

Keywords: Caesarean section; Length of stay; Machine learning; Public health; Regression model.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: In compliance with the Declaration of Helsinki and with the Italian Legislative Decree 211/2003, Implementation of the 2001/20/CE directive, since no patients/children were involved in the study, the signed informed consent form and ethical approval are not mandatory for these types of studies. Furthermore, in compliance with the regulations of the Italian National Institute of Health, our study is not reported among those needing assessment by the Ethical Committee of the Italian National Institute of Health. Consent for publication: Not applicable. Consent to participate: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Summary description of the dataset
Fig. 2
Fig. 2
Partial regression plot of the MLR model
Fig. 3
Fig. 3
Homoscedasticity of the data
Fig. 4
Fig. 4
ROC curves
See this image and copyright information in PMC

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