Comparison of machine learning techniques for mortality prediction in a prospective cohort of older adults

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dc.contributor.authorTedesco, Salvatore
dc.contributor.authorAndrulli, Martina
dc.contributor.authorLarsson, Markus Ãkerlund
dc.contributor.authorKelly, Daniel
dc.contributor.authorTimmons, Suzanne
dc.contributor.authorBarton, John
dc.contributor.authorCondell, Joan
dc.contributor.authorO'Flynn, Brendan
dc.contributor.authorNordström, Anna
dc.contributor.funderEuropean Regional Development Funden
dc.contributor.funderInterregen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEnterprise Irelanden
dc.contributor.funderDepartment of Business, Enterprise and Innovation, Irelanden
dc.date.accessioned2022-03-16T11:36:11Z
dc.date.available2022-03-16T11:36:11Z
dc.date.issued2021-12-04
dc.date.updated2022-03-16T09:58:23Z
dc.description.abstractAs global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all-cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all-cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free-living settings, obtained for the “Healthy Ageing Initiative” study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random UnderSampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data-driven and disease-agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.en
dc.description.sponsorshipEuropean Regional Development Fund (ERDF under Ireland’s European Structural and Investment Funds Programmes 2014–2020; INTERREG Northern Periphery and Arctic (NPA) funded project SenDOC (Grant number 95)); Science Foundation Ireland (Grant number 12/RC/2289-P2 INSIGHT-2 and 13/RC/2077 CONNECT which are co-funded under the ERDF); Enterprise Ireland and the Department of Business, Enterprise and Innovation (under the DTIF project HOLISTICS)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid12806en
dc.identifier.citationTedesco, S., Andrulli, M., Larsson, M. Å., Kelly, D., Alamäki, A., Timmons, S., Barton, J., Condell, J., O’Flynn, B. and Nordström, A. (2021) ‘Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults’, International Journal of Environmental Research and Public Health. International Journal of Environmental Research and Public Health, 18(23), 12806 (18 pp). doi: 10.3390/ijerph182312806en
dc.identifier.doi10.3390/ijerph182312806en
dc.identifier.endpage18en
dc.identifier.issn1660-4601
dc.identifier.issued23en
dc.identifier.journaltitleInternational Journal of Environmental Research and Public Healthen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/12931
dc.identifier.volume18en
dc.language.isoenen
dc.publisherMDPIen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/en
dc.relation.urihttps://www.mdpi.com/1660-4601/18/23/12806/htm
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectAgeingen
dc.subjectAll-cause mortalityen
dc.subjectImbalanced dataen
dc.subjectMachine learningen
dc.subjectMortality predictionen
dc.subjectOlder adultsen
dc.subjectPrediction modelsen
dc.subjectAcute physiologyen
dc.subjectIntensive-careen
dc.subjectAlgorithmen
dc.subjectSelectionen
dc.subjectIndexen
dc.titleComparison of machine learning techniques for mortality prediction in a prospective cohort of older adultsen
dc.typeArticle (peer-reviewed)en
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