Supervised Machine Learning Insights into Social and Linguistic Influences on Cesarean Rates in Luxembourg
Cesarean sections (CSs) are essential in certain medical contexts but, when overused, can carry risks for both the mother and child. In the unique multilingual landscape of Luxembourg, this study explores whether non-medical factors—such as the language spoken—affect CS rates. Through a survey condu...
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MDPI AG
2025-04-01
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| author | Prasad Adhav María Bélen Farias |
| author_facet | Prasad Adhav María Bélen Farias |
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| description | Cesarean sections (CSs) are essential in certain medical contexts but, when overused, can carry risks for both the mother and child. In the unique multilingual landscape of Luxembourg, this study explores whether non-medical factors—such as the language spoken—affect CS rates. Through a survey conducted with women in Luxembourg, we first applied statistical methods to investigate the influence of various social and linguistic parameters on CS. Additionally, we explored how these factors relate to the feelings of happiness and respect women experience during childbirth. Subsequently, we employed four machine learning models to predict CS based on the survey data. Our findings reveal that women who speak Spanish have a statistically higher likelihood of undergoing a CS than women that do not report speaking that language. Furthermore, those who had CS report feeling less happy and respected compared to those with vaginal births. With both limited and augmented data, our models achieve an average accuracy of approximately 81% in predicting CS. While this study serves as an initial exploration into the social aspects of childbirth, it underscores the need for larger-scale studies to deepen our understanding and to inform policy-makers and health practitioners that support women during their pregnancies and births. This preliminary research advocates for further investigation to address this complex social issue comprehensively. |
| format | Article |
| id | doaj-art-3026c885c1ca4fa4a3bc5bb69dc22e47 |
| institution | Kabale University |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-3026c885c1ca4fa4a3bc5bb69dc22e472025-08-20T03:47:48ZengMDPI AGComputation2079-31972025-04-0113510610.3390/computation13050106Supervised Machine Learning Insights into Social and Linguistic Influences on Cesarean Rates in LuxembourgPrasad Adhav0María Bélen Farias1Luxembourg Researchers Hub a.s.b.l, 223 rue de Luxembourg, 4222 Esch-sur-Alzette, LuxembourgLuxembourg Researchers Hub a.s.b.l, 223 rue de Luxembourg, 4222 Esch-sur-Alzette, LuxembourgCesarean sections (CSs) are essential in certain medical contexts but, when overused, can carry risks for both the mother and child. In the unique multilingual landscape of Luxembourg, this study explores whether non-medical factors—such as the language spoken—affect CS rates. Through a survey conducted with women in Luxembourg, we first applied statistical methods to investigate the influence of various social and linguistic parameters on CS. Additionally, we explored how these factors relate to the feelings of happiness and respect women experience during childbirth. Subsequently, we employed four machine learning models to predict CS based on the survey data. Our findings reveal that women who speak Spanish have a statistically higher likelihood of undergoing a CS than women that do not report speaking that language. Furthermore, those who had CS report feeling less happy and respected compared to those with vaginal births. With both limited and augmented data, our models achieve an average accuracy of approximately 81% in predicting CS. While this study serves as an initial exploration into the social aspects of childbirth, it underscores the need for larger-scale studies to deepen our understanding and to inform policy-makers and health practitioners that support women during their pregnancies and births. This preliminary research advocates for further investigation to address this complex social issue comprehensively.https://www.mdpi.com/2079-3197/13/5/106cesarean sections (CSs)cesarean deliveriesmachine learningmultilingualhealthcarechildbirth |
| spellingShingle | Prasad Adhav María Bélen Farias Supervised Machine Learning Insights into Social and Linguistic Influences on Cesarean Rates in Luxembourg Computation cesarean sections (CSs) cesarean deliveries machine learning multilingual healthcare childbirth |
| title | Supervised Machine Learning Insights into Social and Linguistic Influences on Cesarean Rates in Luxembourg |
| title_full | Supervised Machine Learning Insights into Social and Linguistic Influences on Cesarean Rates in Luxembourg |
| title_fullStr | Supervised Machine Learning Insights into Social and Linguistic Influences on Cesarean Rates in Luxembourg |
| title_full_unstemmed | Supervised Machine Learning Insights into Social and Linguistic Influences on Cesarean Rates in Luxembourg |
| title_short | Supervised Machine Learning Insights into Social and Linguistic Influences on Cesarean Rates in Luxembourg |
| title_sort | supervised machine learning insights into social and linguistic influences on cesarean rates in luxembourg |
| topic | cesarean sections (CSs) cesarean deliveries machine learning multilingual healthcare childbirth |
| url | https://www.mdpi.com/2079-3197/13/5/106 |
| work_keys_str_mv | AT prasadadhav supervisedmachinelearninginsightsintosocialandlinguisticinfluencesoncesareanratesinluxembourg AT mariabelenfarias supervisedmachinelearninginsightsintosocialandlinguisticinfluencesoncesareanratesinluxembourg |