Exploring the most important factors related to self-perceived health among older men in Sweden: a cross-sectional study using machine learning.

Olsson, M; Currow, DC; Ekström, MP

Exploring the most important factors related to self-perceived health among older men in Sweden: a cross-sectional study using machine learning.

Olsson, M Currow, DC Ekström, MP

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Publisher:: BMJ Journals
Publication Type:: Journal Article
Citation:: BMJ Open, 2022, 12, (6), pp. 1-9
Issue Date:: 2022-06-21

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Field	Value	Language
dc.contributor.author	Olsson, M
dc.contributor.author	Currow, DC
dc.contributor.author	Ekström, MP
dc.date.accessioned	2022-10-06T04:56:59Z
dc.date.available	2022-10-06T04:56:59Z
dc.date.issued	2022-06-21
dc.identifier.citation	BMJ Open, 2022, 12, (6), pp. 1-9
dc.identifier.issn	2044-6055
dc.identifier.issn	2044-6055
dc.identifier.uri	http://hdl.handle.net/10453/162359
dc.description.abstract	OBJECTIVE: To evaluate which factors are the most strongly related to self-perceived health among older men and describe the shape of the association between the related factors and self-perceived health using machine learning. DESIGN AND SETTING: This is a cross-sectional study within the population-based VAScular and Chronic Obstructive Lung disease study (VASCOL) conducted in southern Sweden in 2019. PARTICIPANTS: A total of 475 older men aged 73 years from the VASCOL dataset. MEASURES: Self-perceived health was measured using the first item of the Short Form 12. An extreme gradient-boosting model was trained to classify self-perceived health as better (rated: excellent or very good) or worse (rated: fair or poor) using self-reported data on 19 prevalent physician-diagnosed health conditions, intensity of 9 symptoms and 9 demographic and lifestyle factors. Importance of factors was measured in SHapley Additive exPlanations absolute mean and higher scores correspond to greater importance. RESULTS: The most important factors for classifying self-perceived health were: pain (0.629), sleep quality (0.595), breathlessness (0.549), fatigue (0.542) and depression (0.526). Health conditions ranked well below symptoms and lifestyle variables. Low levels of symptoms, good sleep quality, regular exercise, alcohol consumption and a body mass index between 22 and 28 were associated with better self-perceived health. CONCLUSIONS: Symptoms are more strongly related to self-perceived health than health conditions, which suggests that the impacts of health conditions are mediated through symptoms, which could be important targets to improve self-perceived health. Machine learning offers a new way to assess composite constructs such as well-being or quality of life.
dc.format	Electronic
dc.language	eng
dc.publisher	BMJ Journals
dc.relation.ispartof	BMJ Open
dc.relation.isbasedon	10.1136/bmjopen-2022-061242
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1103 Clinical Sciences, 1117 Public Health and Health Services, 1199 Other Medical and Health Sciences
dc.subject.mesh	Aged
dc.subject.mesh	Cross-Sectional Studies
dc.subject.mesh	Depression
dc.subject.mesh	Health Status
dc.subject.mesh	Humans
dc.subject.mesh	Machine Learning
dc.subject.mesh	Male
dc.subject.mesh	Quality of Life
dc.subject.mesh	Sweden
dc.subject.mesh	Aged
dc.subject.mesh	Cross-Sectional Studies
dc.subject.mesh	Depression
dc.subject.mesh	Health Status
dc.subject.mesh	Humans
dc.subject.mesh	Machine Learning
dc.subject.mesh	Male
dc.subject.mesh	Quality of Life
dc.subject.mesh	Sweden
dc.subject.mesh	Humans
dc.subject.mesh	Cross-Sectional Studies
dc.subject.mesh	Depression
dc.subject.mesh	Health Status
dc.subject.mesh	Quality of Life
dc.subject.mesh	Aged
dc.subject.mesh	Sweden
dc.subject.mesh	Male
dc.subject.mesh	Machine Learning
dc.title	Exploring the most important factors related to self-perceived health among older men in Sweden: a cross-sectional study using machine learning.
dc.type	Journal Article
utslib.citation.volume	12
utslib.location.activity	England
utslib.for	1103 Clinical Sciences
utslib.for	1117 Public Health and Health Services
utslib.for	1199 Other Medical and Health Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Faculty of Health/IMPACCT
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-10-06T04:56:56Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	6

Abstract:

OBJECTIVE: To evaluate which factors are the most strongly related to self-perceived health among older men and describe the shape of the association between the related factors and self-perceived health using machine learning. DESIGN AND SETTING: This is a cross-sectional study within the population-based VAScular and Chronic Obstructive Lung disease study (VASCOL) conducted in southern Sweden in 2019. PARTICIPANTS: A total of 475 older men aged 73 years from the VASCOL dataset. MEASURES: Self-perceived health was measured using the first item of the Short Form 12. An extreme gradient-boosting model was trained to classify self-perceived health as better (rated: excellent or very good) or worse (rated: fair or poor) using self-reported data on 19 prevalent physician-diagnosed health conditions, intensity of 9 symptoms and 9 demographic and lifestyle factors. Importance of factors was measured in SHapley Additive exPlanations absolute mean and higher scores correspond to greater importance. RESULTS: The most important factors for classifying self-perceived health were: pain (0.629), sleep quality (0.595), breathlessness (0.549), fatigue (0.542) and depression (0.526). Health conditions ranked well below symptoms and lifestyle variables. Low levels of symptoms, good sleep quality, regular exercise, alcohol consumption and a body mass index between 22 and 28 were associated with better self-perceived health. CONCLUSIONS: Symptoms are more strongly related to self-perceived health than health conditions, which suggests that the impacts of health conditions are mediated through symptoms, which could be important targets to improve self-perceived health. Machine learning offers a new way to assess composite constructs such as well-being or quality of life.

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http://hdl.handle.net/10453/162359