Nikolaou, Nikolaos und Cea, Donatella und Valizadeh, Mahyar und Staab, Jeroen und Dallavalle, Marco und Piraud, M und Peters, A. und Schneider, Alexandra und Taubenböck, Hannes und Wolf, Kathrin (2024) A machine learning framework for modeling the associations between various environmental features and health. 5th ISEE Europe Young and Early Career Researchers Conference, 2024-06-05 - 2024-06-07, Rennes.
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Kurzfassung
Abstract: Introduction: Human health has been associated with the exposure to several environmental variables as well as socio-economic and neighborhood settings. Yet, their interplay is not adequately analyzed and consequently not well understood. We aimed to build a machine learning (ML) pipeline, able to sufficiently identify the driving environmental, socio-economic and individual factors for cardiovascular health. Methods: For our use case, we included midterm data from the baseline examination of the largest German population-based cohort study NAKO conducted between 2014-19 in 18 study centers. We assigned environmental exposures (e.g. air pollution, air temperature, greenness) and neighborhood factors (e.g. urbanization) based on the participants’ residential addresses. We compared traditional regression approaches (Linear Regression, Elastic Net) with multiple ML algorithms, e.g. neighbor-based methods (K-Nearest Neighbour), Statistical Learning (Support Vector Machine), Ensemble Learning (Random Forest, XGBoost) and Neural Networks to identify the main risk factors for hypertension. Results: Of 101,601 participants included in our analysis, 45% were classified as hypertensive. Most models performed well and comparable with an accuracy ranging from 0.68 (K-Nearest Neighbour) to 0.73 (Support Vector Machine) in our test set. The different approaches identified similar factors as the main drivers for hypertension with highest feature importance for individual characteristics (age, Body Mass Index, sex) followed by environmental (non-optimal temperature, air pollution) and individual socio-economic (income, education) factors. Further neighborhood socio-economic factors (e.g. deprivation, household income) are currently assigned and will be incorporated in the next runs. Discussion: The ML pipeline shall be openly accessible soon for use in epidemiological analysis, specifically with binary health outcomes, but we also plan to incorporate continuous outcomes.
elib-URL des Eintrags: | https://elib.dlr.de/202338/ | ||||||||||||||||||||||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||||||||||||||||||||||||||
Titel: | A machine learning framework for modeling the associations between various environmental features and health | ||||||||||||||||||||||||||||||||||||||||||||
Autoren: |
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Datum: | 2024 | ||||||||||||||||||||||||||||||||||||||||||||
Referierte Publikation: | Nein | ||||||||||||||||||||||||||||||||||||||||||||
Open Access: | Nein | ||||||||||||||||||||||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||||||||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||||||||||||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||||||||||||||||||
Stichwörter: | Environment, exposure, health, machine learning | ||||||||||||||||||||||||||||||||||||||||||||
Veranstaltungstitel: | 5th ISEE Europe Young and Early Career Researchers Conference | ||||||||||||||||||||||||||||||||||||||||||||
Veranstaltungsort: | Rennes | ||||||||||||||||||||||||||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||||||||||||||||||||||
Veranstaltungsbeginn: | 5 Juni 2024 | ||||||||||||||||||||||||||||||||||||||||||||
Veranstaltungsende: | 7 Juni 2024 | ||||||||||||||||||||||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Geowissenschaftl. Fernerkundungs- und GIS-Verfahren, R - Fernerkundung u. Geoforschung | ||||||||||||||||||||||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||||||||||||||||||
Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit | ||||||||||||||||||||||||||||||||||||||||||||
Hinterlegt von: | Staab, Jeroen | ||||||||||||||||||||||||||||||||||||||||||||
Hinterlegt am: | 08 Aug 2024 10:58 | ||||||||||||||||||||||||||||||||||||||||||||
Letzte Änderung: | 08 Aug 2024 10:58 |
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