Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults

Salvatore Tedesco, Martina Andrulli, Markus Åkerlund Larsson, Daniel Kelly, Antti Alamäki, Suzanne Timmons, John Barton, Joan Condell, Brendan O’Flynn and Anna Nordström
Additional contact information
Salvatore Tedesco: Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland
Martina Andrulli: Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland
Markus Åkerlund Larsson: Department of Public Health and Clinical Medicine, Section of Sustainable Health, Umeå University, SE-901 87 Umeå, Sweden
Daniel Kelly: School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry BT48 7JL, UK
Antti Alamäki: Department of Physiotherapy, Karelia University of Applied Sciences, Tikkarinne 9, FI-80200 Joensuu, Finland
Suzanne Timmons: Centre for Gerontology and Rehabilitation, University College Cork, T12XH60 Cork, Ireland
John Barton: Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland
Joan Condell: School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry BT48 7JL, UK
Brendan O’Flynn: Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, T12R5CP Cork, Ireland
Anna Nordström: Department of Public Health and Clinical Medicine, Section of Sustainable Health, Umeå University, SE-901 87 Umeå, Sweden

IJERPH, 2021, vol. 18, issue 23, 1-18

Abstract: 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 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.

Keywords: ageing; all-cause mortality; imbalanced data; machine learning; mortality prediction; older adults; prediction models (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/23/12806/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/23/12806/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:23:p:12806-:d:695129

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().