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Comparison of machine learning techniques for mortality prediction in a prospective cohort of older adults
Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, Cork, Ireland.
Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, Cork, Ireland.
Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Avdelningen för hållbar hälsa.
School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom.
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2021 (Engelska)Ingår i: International Journal of Environmental Research and Public Health, ISSN 1661-7827, E-ISSN 1660-4601, Vol. 18, nr 23, artikel-id 12806Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

As 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 Under‐ Sampling 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.

Ort, förlag, år, upplaga, sidor
MDPI, 2021. Vol. 18, nr 23, artikel-id 12806
Nyckelord [en]
Ageing, All‐cause mortality, Imbalanced data, Machine learning, Mortality prediction, Older adults, Prediction models
Nationell ämneskategori
Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi
Identifikatorer
URN: urn:nbn:se:umu:diva-190284DOI: 10.3390/ijerph182312806ISI: 000735157800001Scopus ID: 2-s2.0-85120607099OAI: oai:DiVA.org:umu-190284DiVA, id: diva2:1620255
Tillgänglig från: 2021-12-15 Skapad: 2021-12-15 Senast uppdaterad: 2023-09-05Bibliografiskt granskad

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Larsson, Markus ÅkerlundNordström, Anna

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International Journal of Environmental Research and Public Health
Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi

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