Predicting factors influencing suicidal ideation among middle aged male wage workers in South Korea: a study using stochastic gradient descent regressor and logistic regression analysis

Background: South Korea has one of the highest suicide rates among Organisation for Economic Co-operation and Development (OECD) countries, posing a significant public health issue. This study aims to identify factors influencing suicidal ideation among middle aged male wage workers in South Korea using the 2019 Korean Health Panel data. Methods: A cross-sectional design was employed, utilizing a sample of 708 middle aged male wage workers aged 40 to 65 years. Socio demographic and health related characteristics were analyzed. The Stochastic Gradient Descent Regressor (SGDR) was used to predict suicidal ideation and its performance was compared with Classification and Regression Trees (CART), Support Vector Machine (SVM) and Naive Bayes models. Variable importance scores from SGDR were used for logistic regression analysis to identify key predictors. Results: The prevalence of suicidal ideation was 12.4%. The SGDR model demonstrated superior performance with an accuracy of 0.82 and Area Under the Curve (AUC) of 0.78. Key predictors included depression, stress, anxiety, lower education levels, temporary employment and poor self-rated health. Logistic regression analysis showed significant associations with adjusted odds ratios (ORs) ranging from 1.45 to 4.38. Conclusions: By identifying key predictors and employing advanced predictive models, this study offers valuable insights for policymakers and healthcare providers to design targeted interventions and support systems for at-risk individuals.