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Predicting 3-year depressive symptoms among middle-aged and older adults in rural China using random forest: insights from the China health and retirement longitudinal study | BMC Psychology

Predicting 3-year depressive symptoms among middle-aged and older adults in rural China using random forest: insights from the China health and retirement longitudinal study | BMC Psychology
  • Herrman H, Patel V, Kieling C, Berk M, Buchweitz C, Cuijpers P, et al. Time for united action on depression: a Lancet–World Psychiatric Association commission. Lancet. 2022;399:957–1022.

    Article 
    PubMed 

    Google Scholar 

  • Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B. Depression, chronic diseases, and decrements in health: results from the world health surveys. Lancet. 2007;370:851–8.

    Article 
    PubMed 

    Google Scholar 

  • Jacobson NC, Newman MG. Anxiety and depression as bidirectional risk factors for one another: a meta-analysis of longitudinal studies. Psychol Bull. 2017;143:1155–200.

    Article 
    PubMed 

    Google Scholar 

  • Blair-West GW, Cantor CH, Mellsop GW, Eyeson-Annan ML. Lifetime suicide risk in major depression: sex and age determinants. J Affect Disord. 1999;55:171–8.

    Article 
    PubMed 

    Google Scholar 

  • Malhi GS, Mann JJ. Depression. Lancet. 2018;392:2299–312.

    Article 
    PubMed 

    Google Scholar 

  • Ren X, Yu S, Dong W, Yin P, Xu X, Zhou M. Burden of depression in China, 1990–2017: findings from the global burden of disease study 2017. J Affect Disord. 2020;268:95–101.

    Article 
    PubMed 

    Google Scholar 

  • Liu Q, He H, Yang J, Feng X, Zhao F, Lyu J. Changes in the global burden of depression from 1990 to 2017: findings from the global burden of disease study. J Psychiatr Res. 2020;126:134–40.

    Article 
    PubMed 

    Google Scholar 

  • World Health Organization.. Depression and other common mental disorders: global health estimates. Geneva: World Health Organization. 2017.

  • GBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Psychiatry. 2022;9:137–50.

  • Cheng Y, Zhang XM, Ye SY, Jin HM, Yang XH. Suicide in Chinese graduate students: a review from 2000 to 2019. Front Psychiatry. 2020;11:579745.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hammen C. Risk factors for depression: an autobiographical review. Annu Rev Clin Psychol. 2018;14:1–28.

    Article 
    PubMed 

    Google Scholar 

  • Piccinelli M, Wilkinson G. Gender differences in depression: critical review. Br J Psychiatry. 2000;177:486–92.

    Article 
    PubMed 

    Google Scholar 

  • Burcusa SL, Iacono WG. Risk for recurrence in depression. Clin Psychol Rev. 2007;27:959–85.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Boden JM, Fergusson DM. Alcohol and depression. Addiction. 2011;106:906–14.

    Article 
    PubMed 

    Google Scholar 

  • Wang Y, Li Y, Huang Y, Yi C, Ren J. Housing wealth inequality in China: an urban-rural comparison. Cities. 2020;96:102428.

    Article 

    Google Scholar 

  • Treiman DJ. The difference between Heaven and earth: urban–rural disparities in well-being in China. Res Soc Stratif Mobil. 2012;30:33–47.

    Google Scholar 

  • Zhong S, Wang M, Zhu Y, Chen Z, Huang X. Urban expansion and the urban–rural income gap: empirical evidence from China. Cities. 2022;129:103831.

    Article 

    Google Scholar 

  • Ma X, He Y, Xu J. Urban–rural disparity in prevalence of multimorbidity in China: a cross-sectional nationally representative study. BMJ Open. 2020;10:e038404.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Brinda EM, Rajkumar AP, Attermann J, Gerdtham UG, Enemark U, Jacob KS. Health, social, and economic variables associated with depression among older people in low and middle income countries: world health organization study on global ageing and adult health. Am J Geriatr Psychiatry. 2016;24:1196–208.

    Article 
    PubMed 

    Google Scholar 

  • Ngui EM, Khasakhala L, Ndetei D, Roberts LW. Mental disorders, health inequalities and ethics: a global perspective. Int Rev Psychiatry. 2010;22:235–44.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Liu S, Griffiths SM. From economic development to public health improvement: China faces equity challenges. Public Health. 2011;125:669–74.

    Article 
    PubMed 

    Google Scholar 

  • Kafczyk T, Hämel K. Primary mental healthcare for older people in India: between stigmatization and community orientation. Discov Ment Health. 2023;3:14.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang J, Wang Y, Chen S, Fu T, Sun G. Urban-rural differences in key factors of depressive symptoms among Chinese older adults based on random forest model. J Affect Disord. 2024;344:292–300.

    Article 
    PubMed 

    Google Scholar 

  • Agliata A, Giordano D, Bardozzo F, Bottiglieri S, Facchiano A, Tagliaferri R. Machine learning as a support for the diagnosis of type 2 diabetes. Int J Mol Sci. 2023;24:6775.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Huang C, Jiang L. Data monitoring and sports injury prediction model based on embedded system and machine learning algorithm. Microprocessors and Microsystems. 2021;81:103654.

  • Lin L, Hu X, Liu X, Hu G. Key influences on dysglycemia across fujian’s urban-rural divide. PLoS ONE. 2024;19:e0308073.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lin L, Liu X, Cai C, Zheng Y, Li D, Hu G. Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models. Front Public Health. 2025;13:1597853.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Breiman L. Random forests. Mach Learn. 2001;45:5–32.

    Article 

    Google Scholar 

  • Qasrawi R, Vicuna Polo SP, Abu Al-Halawa D, Hallaq S, Abdeen Z. Assessment and prediction of depression and anxiety risk factors in schoolchildren: machine learning techniques performance analysis. JMIR Form Res. 2022;6:e32736.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Smith KM, Renshaw PF, Bilello J. The diagnosis of depression: current and emerging methods. Compr Psychiatry. 2013;54:1–6.

    Article 
    PubMed 

    Google Scholar 

  • Rettew DC, Lynch AD, Achenbach TM, Dumenci L, Ivanova MY. Meta-analyses of agreement between diagnoses made from clinical evaluations and standardized diagnostic interviews. Int J Methods Psychiatr Res. 2009;18:169–84.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nickson D, Meyer C, Walasek L, Toro C. Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review. BMC Med Inform Decis Mak. 2023;23:271.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nemesure MD, Heinz MV, Huang R, Jacobson NC. Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence. Sci Rep. 2021;11:1980.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Vu T, Dawadi R, Yamamoto M, Tay JT, Watanabe N, Kuriya Y, et al. Prediction of depressive disorder using machine learning approaches: findings from the NHANES. BMC Med Inform Decis Mak. 2025;25:83.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int J Epidemiol. 2014;43:61–8.

    Article 
    PubMed 

    Google Scholar 

  • Andresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depression in well older adults: evaluation of a short form of the CES-D. Am J Prev Med. 1994;10:77–84.

    Article 
    PubMed 

    Google Scholar 

  • Chen H, Mui AC. Factorial validity of the center for epidemiologic studies depression scale short form in older population in China. Int Psychogeriatr. 2014;26:49–57.

    Article 
    PubMed 

    Google Scholar 

  • Lewinsohn PM, Seeley JR, Roberts RE, Allen NB. Center for epidemiologic studies depression scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychol Aging. 1997;12(2):277–87.

    Article 
    PubMed 

    Google Scholar 

  • Park S-H, Lee H. Is the center for epidemiologic studies depression scale as useful as the geriatric depression scale in screening for late-life depression? A systematic review. J Affect Disord. 2021;292:454–63.

    Article 
    PubMed 

    Google Scholar 

  • Fu H, Si L, Guo R. What is the optimal cut-off point of the 10-item center for epidemiologic studies depression scale for screening depression among Chinese individuals aged 45 and over? An exploration using latent profile analysis. Front Psychiatry. 2022;13:820777.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Haibo He Y, Bai, Garcia EA, Shutao Li ADASYN. Adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). Hong Kong, China: IEEE; 2008. pp. 1322–8.

  • Machado MO, Veronese N, Sanches M, Stubbs B, Koyanagi A, Thompson T, et al. The association of depression and all-cause and cause-specific mortality: an umbrella review of systematic reviews and meta-analyses. BMC Med. 2018;16:112.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Granitto PM, Furlanello C, Biasioli F, Gasperi F. Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometrics Intell Lab Syst. 2006;83:83–90.

    Article 

    Google Scholar 

  • Hamada M, Tanimu JJ, Hassan M, Kakudi HA, Robert P. Evaluation of recursive feature elimination and LASSO regularization-based optimized feature selection approaches for cervical cancer prediction. IEEE 14th International Symposium on Embedded Multicore/many-core Systems-on-chip (mcsoc). Singapore, Singapore: IEEE. 2021;2021:333–9.

    Google Scholar 

  • Pudjihartono N, Fadason T, Kempa-Liehr AW, O’Sullivan JM. A review of feature selection methods for machine learning-based disease risk prediction. Front Bioinf. 2022;2:927312.

    Article 

    Google Scholar 

  • Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res. 2012;13 null:281–305.

    Google Scholar 

  • Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26:565–74.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30:4765–4774.

  • Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2:56–67.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Stekhoven DJ, Bühlmann P. Missforest—non-parametric missing value imputation for mixed-type data. Bioinformatics. 2012;28:112–8.

    Article 
    PubMed 

    Google Scholar 

  • Shi M, Zhang X, Wang H. The prevalence of diabetes, prediabetes and associated risk factors in Hangzhou, Zhejiang province: a community-based cross-sectional study. Diabetes Metab Syndr Obes Targets Ther. 2022;15:713–21.

    Article 

    Google Scholar 

  • Keramat SA, Alam K, Rana RH, Chowdhury R, Farjana F, Hashmi R, et al. Obesity and the risk of developing chronic diseases in middle-aged and older adults: findings from an Australian longitudinal population survey, 2009–2017. PLoS ONE. 2021;16:e0260158.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wei Z, Wang X, Ren L, Liu C, Liu C, Cao M, et al. Using machine learning approach to predict depression and anxiety among patients with epilepsy in China: a cross-sectional study. J Affect Disord. 2023;336:1–8.

    Article 
    PubMed 

    Google Scholar 

  • Zhang Y, Wang S, Hermann A, Joly R, Pathak J. Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women. J Affect Disord. 2021;279:1–8.

    Article 
    PubMed 

    Google Scholar 

  • Ali MM, Algashamy HAA, Alzidi E, Ahmed K, Bui FM, Patel SK, et al. Development and performance analysis of machine learning methods for predicting depression among menopausal women. Healthcare Analytics. 2023;3:100202.

    Article 

    Google Scholar 

  • Gomes SRBS, Von Schantz M, Leocadio-Miguel M. Predicting depressive symptoms in middle-aged and elderly adults using sleep data and clinical health markers: a machine learning approach. Sleep Med. 2023;102:123–31.

    Article 
    PubMed 

    Google Scholar 

  • Tate AE, McCabe RC, Larsson H, Lundström S, Lichtenstein P, Kuja-Halkola R. Predicting mental health problems in adolescence using machine learning techniques. PLoS ONE. 2020;15:e0230389.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Speiser JL, Callahan KE, Ip EH, Miller ME, Tooze JA, Kritchevsky SB, et al. Predicting future mobility limitation in older adults: a machine learning analysis of health ABC study data. The Journals of Gerontology: Series A. 2022;77:1072–8.

    Google Scholar 

  • Hu G, Lin L, Hu X, Zheng Y, Liu X, Xu Z, et al. Machine learning-based diagnosis of type 2 diabetes mellitus using social determinants of health. Mol Cell Biomech. 2025;22:1461.

    Article 

    Google Scholar 

  • Kessler R. Sex and depression in the National comorbidity survey I: lifetime prevalence, chronicity and recurrence. J Affect Disord. 1993;29:85–96.

    Article 
    PubMed 

    Google Scholar 

  • Noble RE. Depression in women. Metabolism. 2005;54:49–52.

    Article 
    PubMed 

    Google Scholar 

  • Vink D, Aartsen MJ, Comijs HC, Heymans MW, Penninx BWJH, Stek ML, et al. Onset of anxiety and depression in the aging population: comparison of risk factors in a 9-year prospective study. Am J Geriatr Psychiatry. 2009;17:642–52.

    Article 
    PubMed 

    Google Scholar 

  • Gao X, Geng T, Jiang M, Huang N, Zheng Y, Belsky DW, et al. Accelerated biological aging and risk of depression and anxiety: evidence from 424,299 UK biobank participants. Nat Commun. 2023;14:2277.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ibrahim AK, Kelly SJ, Glazebrook C. Socioeconomic status and the risk of depression among UK higher education students. Soc Psychiatry Psychiatr Epidemiol. 2013;48:1491–501.

    Article 
    PubMed 

    Google Scholar 

  • the Japan Environment and Children’s Study (JECS) Group, Matsumura K, Hamazaki K, Tsuchida A, Kasamatsu H, Inadera H. Education level and risk of postpartum depression: results from the Japan environment and children’s study (JECS). BMC Psychiatry. 2019;19:419.

    Article 

    Google Scholar 

  • De Andrade TB, Bof De Andrade F, Viana MC. Prevalence of depressive symptoms and its association with social support among older adults: the Brazilian national health survey. J Affect Disord. 2023;333:468–73.

    Article 
    PubMed 

    Google Scholar 

  • Wickramaratne PJ, Yangchen T, Lepow L, Patra BG, Glicksburg B, Talati A, et al. Social connectedness as a determinant of mental health: a scoping review. PLoS ONE. 2022;17:e0275004.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Steger MF, Kashdan TB. Depression and everyday social activity, belonging, and well-being. J Couns Psychol. 2009;56:289–300.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chang-Quan H, Xue-Mei Z, Bi-Rong D, Zhen-Chan L, Ji-Rong Y, Qing-Xiu L. Health status and risk for depression among the elderly: a meta-analysis of published literature. Age Ageing. 2010;39:23–30.

    Article 
    PubMed 

    Google Scholar 

  • España-Romero V, Artero EG, Lee D, Sui X, Baruth M, Ruiz JR, et al. A prospective study of ideal cardiovascular health and depressive symptoms. Psychosomatics. 2013;54:525–35.

    Article 
    PubMed 

    Google Scholar 

  • Chang S-C, Pan A, Kawachi I, Okereke OI. Risk factors for late-life depression: a prospective cohort study among older women. Prev Med. 2016;91:144–51.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Dominick CH, Blyth FM, Nicholas MK. Pain. 2012;153:293–304.

    Article 
    PubMed 

    Google Scholar 

  • Yuan W, Xie Z, Dong P, Yang Y. Linking perceived social support to self-esteem and social integration among adolescents with visual impairment: a cross-lagged study. Front Psychol. 2022;13:1054857.

    Article 
    PubMed 

    Google Scholar 

  • Howard DM, Adams MJ, Clarke T-K, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22:343–52.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Li Z, Ruan M, Chen J, Fang Y. Major depressive disorder: advances in neuroscience research and translational applications. Neurosci Bull. 2021;37:863–80.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kim Y-K, Myint A-M. Clinical application of low serum cholesterol as an indicator for suicide risk in major depression. J Affect Disord. 2004;81:161–6.

    Article 
    PubMed 

    Google Scholar 

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