Research Output

The use of data mining to identify indicators of health-related quality of life in patients with irritable bowel syndrome

  Aim.  To examine the health-related quality of life in a cohort of individuals with irritable bowel syndrome and to explore the use of several data-mining methods to identify which socio-demographic and irritable bowel syndrome symptoms are most highly associated with impaired health-related quality of life.

Background.  Health-related quality of life can be adversely affected by irritable bowel syndrome. Little is presently known about the predictive factors that may influence the quality of life in these patients.

Design.  Cross-sectional survey design involving the general population of the UK.

Methods.  Individuals with symptoms of irritable bowel syndrome were recruited to a longitudinal cohort survey via a UK-wide newspaper advert. Health-related quality of life was measured using a battery of validated questionnaires. Several data-mining models to determine which factors are associated with impaired health-related quality of life are considered in this study and include logistic regression, a classification tree and artificial neural networks.

Results.  As well as irritable bowel syndrome symptom severity, results indicate that psychological morbidity and socio-demographic factors such as marital status and employment status also have a major influence on health-related quality of life in irritable bowel syndrome.

Conclusion.  Health-related quality of life is impaired in community-based individuals in the UK with irritable bowel syndrome. Although not always as easily interpreted as logistic regression, data-mining techniques indicate subsets of factors that are highly associated with impaired quality of life. These models tend to include subsets of irritable bowel syndrome symptoms and psychosocial factors.

Relevance to clinical practice.  Identification of the role of psychological and socio-demographic factors on health-related quality of life may provide more insight into the nature of irritable bowel syndrome. Greater understanding of these factors will facilitate more flexible and efficient nursing assessment and management of this patient group

  • Type:

    Article

  • Date:

    31 December 2012

  • Publication Status:

    Published

  • Publisher

    Blackwell Synergy

  • DOI:

    10.1111/j.1365-2702.2011.03897.x

  • ISSN:

    0962-1067

  • Library of Congress:

    RT Nursing

  • Dewey Decimal Classification:

    610.73 Nursing

Citation

Penny, K. I., & Smith, G. D. (2012). The use of data mining to identify indicators of health-related quality of life in patients with irritable bowel syndrome. Journal of Clinical Nursing, 21, 2761-2771. https://doi.org/10.1111/j.1365-2702.2011.03897.x

Authors

Keywords

Artificial neural network; classification tree; data mining; health-related quality of life; irritable bowel syndrome; logistic regression; nursing;

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