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

Abstract

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. This is a cross-sectional study within the population-based VAScular and Chronic Obstructive Lung disease study (VASCOL) conducted in southern Sweden in 2019. A total of 475 older men aged 73 years from the VASCOL dataset. 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: <i>excellent</i> or <i>very good</i>) or worse (rated: <i>fair</i> or <i>poor</i>) using self-reported data on 19 prevalent physician-diagnosed <i>health conditions</i>, intensity of 9 <i>symptoms</i> and 9 <i>demographic and lifestyle factors</i>. Importance of factors was measured in SHapley Additive exPlanations absolute mean and higher scores correspond to greater importance. 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). <i>Health conditions</i> ranked well below <i>symptoms</i> and <i>lifestyle variables</i>. 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. <i>Symptoms</i> are more strongly related to self-perceived health than <i>health conditions</i>, which suggests that the impacts of <i>health conditions</i> are mediated through <i>symptoms</i>, 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.