Graph Machine Learning With Systematic Hyper-Parameter Selection on Hidden Networks and Mental Health Conditions in the Middle-Aged and Old.

Abstract

Objective: It takes significant time and energy to collect data on explicit networks. This study used graph machine learning to identify hidden networks and predict mental health conditions in the middle-aged and old.

Methods: Data came from the Korean Longitudinal Study of Ageing (2016-2018), with 2,000 participants aged 56 or more. The dependent variable was mental disease (no vs. yes) in 2018. Twenty-eight predictors in 2016 were included. Graph machine learning with systematic hyper-parameter selection was conducted.

Results: The area under the curve was similar across different models in different scenarios. However, sensitivity (93%) was highest for the graph random forest in the scenario of 2,000 participants and the centrality requirement of life satisfaction 90. Based on the graph random forest, top-10 determinants of mental disease were mental disease in previous period (2016), age, income, life satisfaction-health, life satisfaction-overall, subjective health, body mass index, life satisfaction-economic, children alive and health insurance. Especially, life satisfaction-overall was a top-5 determinant in the graph random forest, which considers life satisfaction as an emotional connection and a group interaction.

Conclusion: Improving an individual's life satisfaction as a personal condition is expected to strengthen the individual's emotional connection as a group interaction, which would reduce the risk of the individual's mental disease in the end. This would bring an important clinical implication for highlighting the importance of a patient's life satisfaction and emotional connection regarding the diagnosis and management of the patient's mental disease.