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.
|Number of pages||18|
|Journal||International Journal of Environmental Research and Public Health|
|Publication status||Published (in print/issue) - 4 Dec 2021|
Bibliographical noteFunding Information:
Funding: This project is co‐funded by the European Regional Development Fund (ERDF) under Ireland’s European Structural and Investment Funds Programmes 2014–2020. Aspects of this work have been supported in part by INTERREG Northern Periphery and Arctic (NPA) funded project SenDOC (Grant number 95). Aspects of this publication have emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 12/RC/2289‐P2 IN‐ SIGHT‐2 and 13/RC/2077 CONNECT which are co‐funded under the ERDF. Aspects of this publication were supported by Enterprise Ireland and the Department of Business, Enterprise and Innovation under the DTIF project HOLISTICS.
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- all-cause mortality
- imbalanced data
- machine learning
- mortality prediction
- older adults
- prediction models