Published on in Vol 8, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20995, first published .
Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

Debbie Rankin 1, BSc, PhD;  Michaela Black 1, BSc, PhD;  Bronac Flanagan 1, BSc, MSc, PhD;  Catherine F Hughes 2, BSc, PhD;  Adrian Moore 3, BSc, MSc, PhD;  Leane Hoey 2, BSc, MSc, PhD;  Jonathan Wallace 4, BA, MSc;  Chris Gill 2, BSc, PhD;  Paul Carlin 5, BSc, MPA;  Anne M Molloy 6, PhD;  Conal Cunningham 7, MD;  Helene McNulty 2, BSc, PhD

1 School of Computing, Engineering and Intelligent Systems, Ulster University , Derry~Londonderry , GB

2 School of Biomedical Sciences, Nutrition Innovation Centre for Food and Health, Ulster University, Coleraine , GB

3 School of Geography and Environmental Sciences, Ulster University , Coleraine , GB

4 School of Computing, Ulster University , Jordanstown , GB

5 School of Health, Wellbeing and Social Care, The Open University , Belfast , GB

6 School of Medicine, Trinity College Dublin , Dublin , IE

7 Mercers Institute for Research on Ageing, St James's Hospital , Dublin , IE

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