Missing data represents a challenge in large-scale epidemiological studies as it can introduce a strong and negative bias in the final estimates when not handled appropriately. Addressing missing values is considered important for the correct assignment of cases from one hand and the characterisation of risk factors from another. In this study, we present a robust experimental comparison between MICE and several ML-based imputation approaches applied to the Ecuadorian birth data. We assess their performance and discuss the respective strengths and limitations within an epidemiological context.
Missing data imputation in epidemiology: a comparison between MICE and Machine Learning methods
Franco Alberto Cardillo
2026
Abstract
Missing data represents a challenge in large-scale epidemiological studies as it can introduce a strong and negative bias in the final estimates when not handled appropriately. Addressing missing values is considered important for the correct assignment of cases from one hand and the characterisation of risk factors from another. In this study, we present a robust experimental comparison between MICE and several ML-based imputation approaches applied to the Ecuadorian birth data. We assess their performance and discuss the respective strengths and limitations within an epidemiological context.| Campo DC | Valore | Lingua |
|---|---|---|
| dc.authority.orgunit | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | en |
| dc.authority.people | Mahmoud Hashoush | en |
| dc.authority.people | Emmanuelle Cadot | en |
| dc.authority.people | Franco Alberto Cardillo | en |
| dc.authority.project | corda_____he::86c21b1aa82d5bdc53411947d7ebd9f8 | en |
| dc.collection.id.s | 2e1a85b5-484d-45dd-a997-50e67e31babd | * |
| dc.collection.name | 04.05 Poster/Abstract non pubblicati in atti di convegno | * |
| dc.contributor.appartenenza | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | * |
| dc.contributor.appartenenza.mi | 918 | * |
| dc.contributor.area | Non assegn | * |
| dc.date.firstsubmission | 2026/03/04 11:36:34 | * |
| dc.date.issued | 2026 | - |
| dc.date.submission | 2026/03/04 11:36:34 | * |
| dc.description.abstracteng | Missing data represents a challenge in large-scale epidemiological studies as it can introduce a strong and negative bias in the final estimates when not handled appropriately. Addressing missing values is considered important for the correct assignment of cases from one hand and the characterisation of risk factors from another. In this study, we present a robust experimental comparison between MICE and several ML-based imputation approaches applied to the Ecuadorian birth data. We assess their performance and discuss the respective strengths and limitations within an epidemiological context. | - |
| dc.description.allpeople | Hashoush, Mahmoud; Cadot, Emmanuelle; Cardillo, Franco Alberto | - |
| dc.description.allpeopleoriginal | Mahmoud Hashoush, Emmanuelle Cadot, Franco Alberto Cardillo | en |
| dc.description.fulltext | none | en |
| dc.description.numberofauthors | 3 | - |
| dc.identifier.source | manual | * |
| dc.identifier.uri | https://hdl.handle.net/20.500.14243/570982 | - |
| dc.language.iso | eng | en |
| dc.relation.conferencename | EGU General Assembly 2026 | en |
| dc.relation.projectAcronym | STARWARS | en |
| dc.relation.projectAwardNumber | 101086252 | en |
| dc.relation.projectAwardTitle | STormwAteR and WastewAteR networkS heterogeneous data AI-driven management | en |
| dc.relation.projectFunderName | European Commission | en |
| dc.relation.projectFundingStream | Horizon Europe Framework Programme | en |
| dc.subject.keywordseng | Missing data imputation, machine learning | - |
| dc.subject.singlekeyword | Missing data imputation | * |
| dc.subject.singlekeyword | machine learning | * |
| dc.title | Missing data imputation in epidemiology: a comparison between MICE and Machine Learning methods | en |
| dc.type.driver | info:eu-repo/semantics/conferenceObject | - |
| dc.type.full | 04 Contributo in convegno::04.05 Poster/Abstract non pubblicati in atti di convegno | it |
| dc.type.miur | -2 | - |
| iris.orcid.lastModifiedDate | 2026/03/04 11:36:34 | * |
| iris.orcid.lastModifiedMillisecond | 1772620594236 | * |
| iris.sitodocente.maxattempts | 1 | - |
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