Understanding drivers of loneliness: machine learning insights from the HILDA survey

Background: Loneliness has emerged as a pervasive public health challenge. Understanding loneliness and its associated risk factors is crucial for developing interventions to address this issue effectively. This study aimed to investigate loneliness among adults living in Australia, comparing different age cohorts. Method: This study used 10,815, 11,234, 14,670 and 15,049 records with loneliness measurements taken at 2006, 2010, 2014 and 2018, respectively, from the Household, Income and Labour Dynamics in Australia (HILDA) survey. A supervised machine learning algorithm, CatBoost, was employed to predict loneliness. Model predictions were explained using SHapley Additive exPlanations (SHAP) and partial dependence plots across five age-based subgroups to capture life stage variations. Results: Mental well-being, having a life partner, social connectedness and social fulfilment were the most important predictors of loneliness at the whole-population level. Among young adults, the level of friendship fulfilment, financial satisfaction and health status were relatively strong predictors of loneliness, while loneliness in older adults was more strongly associated with spare time fulfilment, community satisfaction and the loss of loved ones. Youth who reported that they did not have a lot of friends were predicted to have a 46.5% (95% CI: 45.9%–47.2%) chance of experiencing loneliness. Seniors have a 44.9% (95% CI: 43.9%–45.8%) chance of experiencing loneliness if they were almost always not fulfilled in their spare time. Implications: This study underscores the need to recognise the heterogeneity of loneliness across the lifecourse and the importance of both targeted strategies and efforts to improve broader social cohesion.