Exploring key factors influencing depressive symptoms among middle-aged and elderly adult population: A machine learning-based method

Thu Tran; Yi Zhen Tan; Sapphire Lin; Fang Zhao; Yee Sien Ng; Dong Ma; Jeonggil Ko; Rajesh Balan

doi:10.1016/j.archger.2024.105647

Exploring key factors influencing depressive symptoms among middle-aged and elderly adult population: A machine learning-based method

Arch Gerontol Geriatr. 2024 Oct 2:129:105647. doi: 10.1016/j.archger.2024.105647. Online ahead of print.

Authors

Thu Tran¹, Yi Zhen Tan², Sapphire Lin³, Fang Zhao⁴, Yee Sien Ng⁵, Dong Ma², Jeonggil Ko⁶, Rajesh Balan²

Affiliations

¹ School of Computing and Information Systems, Singapore Management University, Singapore. Electronic address: ndttran.2019@phdcs.smu.edu.sg.
² School of Computing and Information Systems, Singapore Management University, Singapore.
³ Centre for Population Health Research and Implementation, SingHealth, Singapore; Duke-NUS Medical School, Singapore.
⁴ Mobile Market Monitor, Singapore; Singapore University of Technology and Design, Singapore.
⁵ Duke-NUS Medical School, Singapore; Singapore General Hospital, SingHealth, Singapore.
⁶ School of Integrated Technology, Yonsei University, Republic of Korea.

PMID: 39369564
DOI: 10.1016/j.archger.2024.105647

Abstract

Objective: This paper aims to investigate the key factors, including demographics, socioeconomics, physical well-being, lifestyle, daily activities and loneliness that can impact depressive symptoms in the middle-aged and elderly population using machine learning techniques. By identifying the most important predictors of depressive symptoms through the analysis, the findings can have important implications for early depression detection and intervention.

Participants: For our cross-sectional study, we recruited a total of 976 volunteers, with a specific focus on individuals aged 50 and above. Each participant was requested to provide their demographic, socioeconomic information and undergo several physical health tests. Additionally, they were asked to respond to questionnaires that assessed their mental well-being. Furthermore, participants were requested to maintain an activity log for a continuous 14-day period, starting from the day after they signed up. They had the option to use either a provided mobile application or paper to record their activities.

Methods: We evaluated multiple machine learning models to find the best-performing one. Subsequently, we conducted post-hoc analysis to extract the variable significance from the selected model to gain deeper insights into the factors influencing depression.

Results: Logistic Regression was chosen as it exhibited superior performance across other models, with AUC of 0.807 ± 0.038, accuracy of 0.798 ± 0.048, specificity of 0.795 ± 0.061, sensitivity of 0.819 ± 0.097, NPV of 0.972 ± 0.013 and PPV of 0.359 ± 0.064. The top influential predictors identified in the model included loneliness, health indicator (i.e. frailty, eyesight, functional mobility), time spent on activities (i.e. staying home, doing exercises and visiting friends) and perceived income adequacy.

Conclusion: These findings have the potential to identify individuals at risk of depression and prioritize interventions based on the influential factors. The amount of time dedicated to daily activities emerges as a significant indicator of depression risk among middle-aged and elderly individuals, along with loneliness, physical health indicators and perceived income adequacy.

Keywords: Depression; Machine learning; Prediction model; Regression model.