Akerlund, Marus

Alamäki, Antti

Andrulli, Martina

Barton, John

Condell, Joan

Kelly, Daniel

Nordstrom, Anna

Tedesco, Salvatore

English

Cancer is an aggressive disease which imparts a tremendous socio-economic burden on the international community. Early detection is an important aspect in improving survival rates for cancer sufferers; however, very few studies have investigated the possibility of predicting which people have the highest risk to develop this disease, even years before the traditional symptoms first occur. In this paper, a dataset from a longitudinal study which was collected among 2291 70-year olds in Sweden has been analyzed to investigate the possibility for predicting 2-7 year cancer-specific mortality. A tailored ensemble model has been developed in order to tackle this highly imbalanced dataset. The performance with different feature subsets has been investigated to evaluate the impact that heterogeneous data sources may have on the overall model. While a full-features model shows an AUC-ROC of 0.882, it is also highlighted that a feature subset which was only including demographics, a questionnaire, and wearable dataset collected in free-living environments presents similar performance (AUC-ROC: 0.857). This analysis confirms the importance of wearable technology for providing unbiased health markers and proved its possible use in the accurate prediction of 2-7 year cancer-related mortality in older adults

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Investigation of the use of wearable data for cancer-specific mortality prediction in older adults

https://pure.ulster.ac.uk/files/91630518/conference_EMBS1_2.pdf

Investigation of the use of wearable data for cancer-specific mortality prediction in older adults

Abstract

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