Use of artificial intelligence to study the hospitalization of women undergoing caesarean section
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...
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| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Public Health |
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| Online Access: | https://doi.org/10.1186/s12889-025-21530-z |
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| author | Arianna Scala Giuseppe Bifulco Anna Borrelli Rosanna Egidio Maria Triassi Giovanni Improta |
| author_facet | Arianna Scala Giuseppe Bifulco Anna Borrelli Rosanna Egidio Maria Triassi Giovanni Improta |
| author_sort | Arianna Scala |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0699875582614f8ebf66d3f2d8b4baf8 |
| institution | Kabale University |
| issn | 1471-2458 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj-art-0699875582614f8ebf66d3f2d8b4baf82025-01-26T12:56:30ZengBMCBMC Public Health1471-24582025-01-012511910.1186/s12889-025-21530-zUse of artificial intelligence to study the hospitalization of women undergoing caesarean sectionArianna Scala0Giuseppe Bifulco1Anna Borrelli2Rosanna Egidio3Maria Triassi4Giovanni Improta5Department of Public Health, University of Naples Federico IIDepartment of Public Health, University of Naples Federico II“Federico II” University Hospital“Federico II” University HospitalDepartment of Public Health, University of Naples Federico IIDepartment of Public Health, University of Naples Federico IIAbstract 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.https://doi.org/10.1186/s12889-025-21530-zCaesarean sectionMachine learningRegression modelPublic healthLength of stay |
| spellingShingle | Arianna Scala Giuseppe Bifulco Anna Borrelli Rosanna Egidio Maria Triassi Giovanni Improta Use of artificial intelligence to study the hospitalization of women undergoing caesarean section BMC Public Health Caesarean section Machine learning Regression model Public health Length of stay |
| title | Use of artificial intelligence to study the hospitalization of women undergoing caesarean section |
| title_full | Use of artificial intelligence to study the hospitalization of women undergoing caesarean section |
| title_fullStr | Use of artificial intelligence to study the hospitalization of women undergoing caesarean section |
| title_full_unstemmed | Use of artificial intelligence to study the hospitalization of women undergoing caesarean section |
| title_short | Use of artificial intelligence to study the hospitalization of women undergoing caesarean section |
| title_sort | use of artificial intelligence to study the hospitalization of women undergoing caesarean section |
| topic | Caesarean section Machine learning Regression model Public health Length of stay |
| url | https://doi.org/10.1186/s12889-025-21530-z |
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