{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T17:46:59Z","timestamp":1786556819439,"version":"build-2736575974"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:00:00Z","timestamp":1740096000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004829","name":"Science and Technology Department of Sichuan Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>It has been increasingly recognized that adults living alone have a higher likelihood of developing Major Depressive Disorder (MDD) than those living with others. However, there is still no prediction model for MDD specifically designed for adults who live alone.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>This study aims to investigate the effectiveness of utilizing personal health data in combination with a stacked ensemble machine learning (SEML) technique to detect MDD among adults living alone, seeking to gain insights into the interaction between personal health data and MDD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our data originated from the US National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2018. We finally selected a set of 30 easily accessible variables encompassing demographic profiles, lifestyle factors, and baseline health conditions. We constructed a SEML model for MDD detection, incorporating three conventional machine learning algorithms as base models and a Neural Network (NN) as the meta-model. Furthermore, SHapley Additive exPlanations (SHAP) analysis was used to explain the impact of each predictor on MDD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The study included 2,642 adult participants who lived alone, of whom 10.6% (279 out of 2,642) had a PHQ-9 score of 10 or above, indicating the presence of MDD. The performance of our SEML model was robust, with an area under the curve (AUC) of 0.85. Further analysis using SHAP revealed positive correlations between the occurrence of MDD and factors such as sleep disorders, number of prescription medications, need for specific walking aids, leak urine during nonphysical activities, chronic bronchitis, and Healthy Eating Index (HEI) scores for sodium. Conversely, age, the Family Monthly Poverty Level Index (FMMPI), and HEI scores for added sugar showed negative correlations with MDD occurrence. Additionally, a U-shaped relationship was noted between the occurrence of MDD and both sleep duration and Body Mass Index (BMI), as well as HEI scores for dairy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The study has successfully developed a predictive model for MDD, specifically tailored for adults living alone using a stacked ensemble technique, enhancing the identification of MDD and its risk factors among adults living alone.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpubh.2025.1472050","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T05:19:20Z","timestamp":1740115160000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Identifying major depressive disorder among US adults living alone using stacked ensemble machine learning algorithms"],"prefix":"10.3389","volume":"13","author":[{"given":"Zhao","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiabao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhou","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,2,21]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.15620\/cdc:136451","article-title":"Living alone and feelings of depression among adults age 18 and older","author":"Mykyta","year":"2024","journal-title":"Natl Health Stat Report"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e27344","DOI":"10.2196\/27344","article-title":"Discovery of depression-associated factors from a nationwide population-based survey: epidemiological study using machine learning and network analysis","volume":"23","author":"Nam","year":"2021","journal-title":"J Med Internet Res"},{"key":"ref3","year":"2023"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"2197","DOI":"10.1016\/S0140-6736(12)61689-4","article-title":"Disability-adjusted life years (dalys) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the global burden of disease study 2010","volume":"380","author":"Murray","year":"2012","journal-title":"Lancet"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1002\/j.2051-5545.2010.tb00298.x","article-title":"Pathophysiology of depression: do we have any solid evidence of interest to clinicians?","volume":"9","author":"Hasler","year":"2010","journal-title":"World Psychiatry"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1186\/s12888-023-05109-9","article-title":"Identifying depression in the United States veterans using deep learning algorithms, nhanes 2005-2018","volume":"23","author":"Qu","year":"2023","journal-title":"BMC Psychiatry"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1016\/j.jad.2019.06.034","article-title":"Identifying depression in the national health and nutrition examination survey data using a deep learning algorithm","volume":"257","author":"Oh","year":"2019","journal-title":"J Affect Disord"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"e288648","DOI":"10.1371\/journal.pone.0288648","article-title":"Machine learning-based identification and related features of depression in patients with diabetes mellitus based on the Korea national health and nutrition examination survey: a cross-sectional study","volume":"18","author":"Lee","year":"2023","journal-title":"PLoS One"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"114261","DOI":"10.1016\/j.psychres.2021.114261","article-title":"Using catboost algorithm to identify middle-aged and elderly depression, national health and nutrition examination survey 2011-2018","volume":"306","author":"Zhang","year":"2021","journal-title":"Psychiatry Res"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.sleep.2023.01.002","article-title":"Predicting depressive symptoms in middle-aged and elderly adults using sleep data and clinical health markers: a machine learning approach","volume":"102","author":"Gomes","year":"2023","journal-title":"Sleep Med"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1056\/NEJMra2302038","article-title":"Artificial intelligence and machine learning in clinical medicine, 2023","volume":"388","author":"Haug","year":"2023","journal-title":"N Engl J Med"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the future - big data, machine learning, and clinical medicine","volume":"375","author":"Obermeyer","year":"2016","journal-title":"N Engl J Med"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"107547","DOI":"10.1016\/j.clineuro.2022.107547","article-title":"Machine learning for outcome prediction of neurosurgical aneurysm treatment: current methods and future directions","volume":"224","author":"Velagapudi","year":"2023","journal-title":"Clin Neurol Neurosurg"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"101934","DOI":"10.1016\/j.eclinm.2023.101934","article-title":"Development and validation of an insulin resistance model for a population without diabetes mellitus and its clinical implication: a prospective cohort study","volume":"58","author":"Tsai","year":"2023","journal-title":"Eclinicalmedicine"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"102544","DOI":"10.1016\/j.conb.2022.102544","article-title":"Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience","volume":"73","author":"Goodwin","year":"2022","journal-title":"Curr Opin Neurobiol"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MCAS.2006.1688199","article-title":"Ensemble based systems in decision making","volume":"6","year":"2006","journal-title":"Ieee Circuits and Systems Magazine"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1177\/00131644221117193","article-title":"Exploration of the stacking ensemble machine learning algorithm for cheating detection in large-scale assessment","volume":"83","author":"Zhou","year":"2023","journal-title":"Educ Psychol Meas"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1007\/s00354-021-00144-0","article-title":"Stacking ensemble-based intelligent machine learning model for predicting post-covid-19 complications","volume":"40","author":"Gupta","year":"2022","journal-title":"N Gener Comput"},{"key":"ref19","year":"2024"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"l1476","DOI":"10.1136\/bmj.l1476","article-title":"Accuracy of patient health questionnaire-9 (phq-9) for screening to detect major depression: individual participant data meta-analysis","volume":"365","author":"Levis","year":"2019","journal-title":"BMJ"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1046\/j.1525-1497.2001.016009606.x","article-title":"The phq-9: validity of a brief depression severity measure","volume":"16","author":"Kroenke","year":"2001","journal-title":"J Gen Intern Med"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1016\/j.jand.2023.05.015","article-title":"Healthy eating index-2020: review and update process to reflect the dietary guidelines for americans,2020-2025","volume":"123","author":"Shams-White","year":"2023","journal-title":"J Acad Nutr Diet"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.ajcnut.2024.08.021","article-title":"Dietaryindex: a user-friendly and versatile r package for standardizing dietary pattern analysis in epidemiological and clinical studies","volume":"120","author":"Zhan","year":"2024","journal-title":"Biorxiv"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"e45776","DOI":"10.2196\/45776","article-title":"Association between joint physical activity and dietary quality and lower risk of depression symptoms in us adults: cross-sectional nhanes study","volume":"9","author":"Liang","year":"2023","journal-title":"JMIR Public Health Surveill"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1186\/s12889-024-18759-5","article-title":"Twenty-four-hour physical activity patterns associated with depressive symptoms: a cross-sectional study using big data-machine learning approach","volume":"24","author":"Nawrin","year":"2024","journal-title":"BMC Public Health"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"1348803","DOI":"10.3389\/fpubh.2023.1348803","article-title":"A machine learning model to predict the risk of depression in us adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study","volume":"11","author":"Li","year":"2023","journal-title":"Front Public Health"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/diagnostics11081429","article-title":"Predicting depression in community dwellers using a machine learning algorithm","volume":"11","author":"Cho","year":"2021","journal-title":"Diagnostics (Basel)"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"e70056","DOI":"10.1111\/cts.70056","article-title":"Practical guide to shap analysis: explaining supervised machine learning model predictions in drug development","volume":"17","author":"Ponce-Bobadilla","year":"2024","journal-title":"Clin Transl Sci"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1017\/S0033291706007719","article-title":"Low prevalence of depression and anxiety is linked to statutory retirement ages rather than personal work exit: a national survey","volume":"36","author":"Villamil","year":"2006","journal-title":"Psychol Med"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1016\/j.jagp.2017.04.004","article-title":"Risk factors for depression: differential across age?","volume":"25","author":"Schaakxs","year":"2017","journal-title":"Am J Geriatr Psychiatry"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.jad.2015.03.009","article-title":"Prevalence of major depressive disorder and socio-demographic correlates: results of a representative household epidemiological survey in Beijing, China","volume":"179","author":"Liu","year":"2015","journal-title":"J Affect Disord"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"2120","DOI":"10.1186\/s12889-021-12168-8","article-title":"Education, household income, and depressive symptoms in middle-aged and older japanese adults","volume":"21","author":"Hinata","year":"2021","journal-title":"BMC Public Health"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"104532","DOI":"10.1016\/j.neubiorev.2022.104532","article-title":"Sleep disorders and non-sleep circadian disorders predict depression: a systematic review and meta-analysis of longitudinal studies","volume":"134","author":"Zhang","year":"2022","journal-title":"Neurosci Biobehav Rev"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.jad.2011.01.011","article-title":"Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies","volume":"135","author":"Baglioni","year":"2011","journal-title":"J Affect Disord"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.jad.2017.06.007","article-title":"The depression and marital status relationship is modified by both age and gender","volume":"223","author":"Bulloch","year":"2017","journal-title":"J Affect Disord"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"6287","DOI":"10.1038\/s41598-017-05649-7","article-title":"Sugar intake from sweet food and beverages, common mental disorder and depression: prospective findings from the Whitehall ii study","volume":"7","author":"Kn\u00fcppel","year":"2017","journal-title":"Sci Rep"},{"key":"ref37","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.jad.2018.11.015","article-title":"Sugar-sweetened beverages consumption and the risk of depression: a meta-analysis of observational studies","volume":"245","author":"Hu","year":"2019","journal-title":"J Affect Disord"},{"key":"ref38","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1037\/0735-7044.120.5.1133","article-title":"Reduced hedonic behavior and altered cardiovascular function induced by mild sodium depletion in rats","volume":"120","author":"Grippo","year":"2006","journal-title":"Behav Neurosci"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1037\/a0018910","article-title":"Mineralocorticoid receptor antagonism prevents hedonic deficits induced by a chronic sodium appetite","volume":"124","author":"Morris","year":"2010","journal-title":"Behav Neurosci"},{"key":"ref40","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.appet.2014.04.008","article-title":"Dietary sodium, added salt, and serum sodium associations with growth and depression in the u.s. general population","volume":"79","author":"Goldstein","year":"2014","journal-title":"Appetite"},{"key":"ref41","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s44220-023-00158-1","article-title":"Body weight in neurological and psychiatric disorders: a large prospective cohort study","volume":"2","author":"Wang","year":"2024","journal-title":"Nature Mental Health"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.jad.2022.06.046","article-title":"The relationship between depressive symptoms and bmi: 2005\u20132018 nhanes data","volume":"313","author":"He","year":"2022","journal-title":"J Affect Disord"},{"key":"ref43","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1001\/archgenpsychiatry.2010.2","article-title":"Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies","volume":"67","author":"Luppino","year":"2010","journal-title":"Arch Gen Psychiatry"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2025.1472050\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T16:08:28Z","timestamp":1740413308000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2025.1472050\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,21]]},"references-count":43,"alternative-id":["10.3389\/fpubh.2025.1472050"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2025.1472050","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,21]]},"article-number":"1472050"}}