World Health Organization (WHO). Noncommunicable diseases. 23 Dec 2024 [cited 18 Mar 2025]. Available: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases
World Health Organization (WHO). The top 10 causes of death. 7 Aug 2024 [cited 18 Mar 2025]. Available: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
Hacker K. The burden of chronic disease. Mayo Clin Proc Innov Qual Outcomes. 2024;8:112–9. https://doi.org/10.1016/j.mayocpiqo.2023.08.005.
Google Scholar
Birk JL, Kronish IM, Moise N, Falzon L, Yoon S, Davidson KW. Depression and multimorbidity: considering Temporal characteristics of the associations between depression and multiple chronic diseases. Health Psychol. 2019;38:802–11. https://doi.org/10.1037/HEA0000737.
Google Scholar
World Health Organization (WHO). Improving access to noncommunicable disease services for Rohingya refugees and immediate host communities in Cox’s Bazar, Bangladesh. [cited 18 Mar 2025]. Available: https://www.who.int/about/accountability/results/who-results-report-2020-mtr/country-story/2021/bangladesh
Mahumud RA, Gow J, Mosharaf MP, Kundu S, Rahman MA, Dukhi N, et al. The burden of chronic diseases, disease-stratified exploration and gender-differentiated healthcare utilisation among patients in Bangladesh. PLoS ONE. 2023;18:e0284117. https://doi.org/10.1371/JOURNAL.PONE.0284117.
Google Scholar
Alam MF, Ahmed HU, Alam MT, Sarkar M, Khan NM, Uddin MJ, et al. Community prevalence of psychiatric disorders: findings from a nationwide survey in Bangladesh. Asian J Psychiatr. 2024;92:103897. https://doi.org/10.1016/J.AJP.2023.103897.
Google Scholar
Wadood MA, Karim MR, Alim SMAHM, Rana MM, Hossain MG. Factors affecting depression among married adults: a gender-based household cross-sectional study. BMC Public Health. 2023;23:1–11. https://doi.org/10.1186/S12889-023-16979-9.
Google Scholar
Rahman MS, Rahman MA, Ali M, Rahman MS, Maniruzzaman M, Yeasmin MA, et al. Determinants of depressive symptoms among older people in Bangladesh. J Affect Disord. 2020;264:157–62. https://doi.org/10.1016/J.JAD.2019.12.025.
Google Scholar
Kamrul-Hasan A, Palash-Molla M, Mainul-Ahsan M, Gaffar A, Asaduzzaman M, Saifuddin M, et al. Prevalence and predictors of depression among patients with type 2 diabetes: A multicenter Cross-sectional study from Bangladesh. Mymensingh Med J. 2019;28:23–30. https://doi.org/10.1016/j.eprac.2024.03.191.
Google Scholar
Islam SMS, Ferrari U, Seissler J, Niessen L, Lechner A. Association between depression and diabetes amongst adults in bangladesh: a hospital based case–control study. J Glob Health. 2015;5:020406. https://doi.org/10.7189/JOGH.05.020406.
Google Scholar
Lotfaliany M, Bowe SJ, Kowal P, Orellana L, Berk M, Mohebbi M. Depression and chronic diseases: Co-occurrence and communality of risk factors. J Affect Disord. 2018;241:461–8. https://doi.org/10.1016/J.JAD.2018.08.011.
Google Scholar
Liu X, Cao H, Zhu H, Zhang H, Niu K, Tang N, et al. Association of chronic diseases with depression, anxiety and stress in Chinese general population: the CHCN-BTH cohort study. J Affect Disord. 2021;282:1278–87. https://doi.org/10.1016/J.JAD.2021.01.040.
Google Scholar
Islam SMS, Rawal LB, Niessen LW. Prevalence of depression and its associated factors in patients with type 2 diabetes: A cross-sectional study in Dhaka, Bangladesh. Asian J Psychiatr. 2015;17:36–41. https://doi.org/10.1016/J.AJP.2015.07.008.
Google Scholar
Mridha MK, Hossain MM, Khan MSA, Hanif AAM, Hasan M, Mitra D, et al. Prevalence and associated factors of depression among adolescent boys and girls in Bangladesh: findings from a nationwide survey. BMJ Open. 2021;11. https://doi.org/10.1136/BMJOPEN-2020-038954.
Disu TR, Anne NJ, Griffiths MD, Mamun MA. Risk factors of geriatric depression among elderly Bangladeshi people: A pilot interview study. Asian J Psychiatr. 2019;44:163–9. https://doi.org/10.1016/J.AJP.2019.07.050.
Google Scholar
Tabassum T, Suzuki T, Iwata Y, Ishiguro H. Depression and associated factors among the elderly population in an urban tertiary geriatric hospital in Bangladesh. Gerontol Geriatr Med. 2023;9. https://doi.org/10.1177/23337214231178145.
Haque MR, Ul Islam MS, Hasan MK, Hossain MS, Hossain Khan MA, Islam F. Determinants of anxiety and depression among Bangladeshi adults during COVID-19 lockdown: an online survey. Heliyon. 2022;8:e09415. https://doi.org/10.1016/j.heliyon.2022.e09415.
Google Scholar
Al-Mamun F, Hasan M, Quadros S, Kaggwa MM, Mubarak M, Sikder MT, et al. Depression among Bangladeshi diabetic patients: a cross-sectional, systematic review, and meta-analysis study. BMC Psychiatry. 2023;23:1–14. https://doi.org/10.1186/S12888-023-04845-2.
Google Scholar
Jahan Y, Khair Z, Moriyama M, Amin MR, Hawlader MDH, Ananta TT, et al. Mental health status among chronic disease patients in Bangladesh during the COVID-19 pandemic: findings from a cross-sectional study. J Family Med Prim Care. 2024;13:2639–46. https://doi.org/10.4103/JFMPC.JFMPC_1608_23.
Google Scholar
Yuen KF, Wang X, Kyriazos T, Poga M. Application of machine learning models in social sciences: managing nonlinear relationships. Encyclopedia. 2024;4:1790–1805 https://doi.org/10.3390/ENCYCLOPEDIA4040118
Hossain S, Hasan MK, Faruk MO, Aktar N, Hossain R, Hossain K. Machine learning approach for predicting cardiovascular disease in Bangladesh: evidence from a cross-sectional study in 2023. BMC Cardiovasc Disord. 2024;24. https://doi.org/10.1186/S12872-024-03883-2.
Uddin MJ, Ahamad MM, Hoque MN, Walid MAA, Aktar S, Alotaibi N, et al. A Comparison of machine learning techniques for the detection of type-2 diabetes mellitus: experiences from Bangladesh. Information. 2023;14(1):376. https://doi.org/10.3390/INFO14070376
Chowdhury AH, Rad D, Rahman MS. Predicting anxiety, depression, and insomnia among Bangladeshi university students using tree-based machine learning models. Health Sci Rep. 2024;7. https://doi.org/10.1002/HSR2.2037.
Siddiqua R, Islam N, Bolaka JF, Khan R, Momen S. AIDA: artificial intelligence based depression assessment applied to Bangladeshi students. Array. 2023;18:100291. https://doi.org/10.1016/J.ARRAY.2023.100291.
Google Scholar
Masud GH, Al, Shanto RI, Sakin I, Kabir MR. Effective depression detection and interpretation: integrating machine learning, deep learning, Language models, and explainable AI. Array. 2025;25:100375. https://doi.org/10.1016/J.ARRAY.2025.100375.
Google Scholar
Azad MS, Leeon SI, Khan R, Mohammed N, Momen S, SAD. Self-assessment of depression for Bangladeshi university students using machine learning and NLP. Array. 2025;25:100372. https://doi.org/10.1016/J.ARRAY.2024.100372.
Google Scholar
Sen SK, Apurba MSA, Mrittika AP, Anwar MT, Al Islam ABMA, Noor J. Unveiling shadows: A data-driven insight on depression among Bangladeshi university students. Heliyon. 2025;11:e41110. https://doi.org/10.1016/J.HELIYON.2024.E41110.
Google Scholar
Nayan M, Uddin M, Hossain M, Alam M, Zinnia M, Haq I, et al. Comparison of the performance of machine learning-based algorithms for predicting depression and anxiety among university students in bangladesh: A result of the first wave of the COVID-19 pandemic. Asian J Social Health Behav. 2022;5:75–84. https://doi.org/10.4103/SHB.SHB_38_22.
Google Scholar
Talukder A, Hasan MM, Haq I, Shariful Islam SM. A machine learning model for the identification of depressive symptoms among university students in Bangladesh. Minerva Psychiatry. 2022;63:237–44. https://doi.org/10.23736/S2724-6612.21.02167-9.
Google Scholar
Munir UB, Kaiser MS, Islam UI, Siddiqui FH. Machine learning classification algorithms for predicting depression among university students in Bangladesh. Lecture Notes Networks Syst. 2022;348:69–80. https://doi.org/10.1007/978-981-16-7597-3_6.
Google Scholar
Motsa MPS, Chiou HY, Chen YH. Association of chronic diseases and lifestyle factors with suicidal ideation among adults aged 18–69 years in eswatini: evidence from a population-based survey. BMC Public Health. 2021;21:2245. https://doi.org/10.1186/S12889-021-12302-6.
Google Scholar
CDC, Adult. BMI Categories. 19 Mar 2024 [cited 27 Mar 2025]. Available: https://www.cdc.gov/bmi/adult-calculator/bmi-categories.html
Mamun MA, Roy N, Gozal D, Almerab MM, Hossain MS, Al Mamun F. Prevalence and associated factors of cigarette smoking and substance use among university entrance test-taking students: A GIS-based study. PLoS ONE. 2024;19:e0308697. https://doi.org/10.1371/JOURNAL.PONE.0308697.
Google Scholar
Moonajilin MS, Kamal MKI, al Mamun F, Safiq MB, Hosen I, Manzar MD, et al. Substance use behavior and its lifestyle-related risk factors in Bangladeshi high school-going adolescents: an exploratory study. PLoS ONE. 2021;16:e0254926. https://doi.org/10.1371/JOURNAL.PONE.0254926.
Google Scholar
Cohen A, Lang JJ, Prince SA, Colley RC, Tremblay MS, Chaput JP. Are adolescents who do physical activity with their parents more active and mentally healthier? Health Rep. 2025;36:19–33. https://doi.org/10.25318/82-003-X202500100002-ENG.
Google Scholar
Kenney EL, Gortmaker SL. United States adolescents’ Television, Computer, Videogame, Smartphone, and tablet use: associations with sugary Drinks, Sleep, physical Activity, and obesity. J Pediatr. 2017;182:144–9. https://doi.org/10.1016/J.JPEDS.2016.11.015.
Google Scholar
Al-Mamun F, Hussain N, Sakib N, Hosen I, Rayhan I, Abdullah AH, et al. Sleep duration during the COVID-19 pandemic in bangladesh: A GIS-based large sample survey study. Sci Rep. 2023;13:1–16. https://doi.org/10.1038/S41598-023-30023-1.
Google Scholar
Jiang B, Tang D, Dai N, Huang C, Liu Y, Wang C, et al. Association of Self-Reported nighttime sleep duration with chronic kidney disease: China health and retirement longitudinal study. Am J Nephrol. 2023;54:249. https://doi.org/10.1159/000531261.
Google Scholar
Meadows G, Harvey C, Fossey E, Burgess P. Assessing perceived need for mental health care in a community survey: development of the perceived need for care questionnaire (PNCQ). Soc Psychiatry Psychiatr Epidemiol. 2000;35:427–35. https://doi.org/10.1007/S001270050260.
Google Scholar
Rahman MA, Dhira TA, Sarker AR, Mehareen J. Validity and reliability of the patient health questionnaire scale (PHQ-9) among university students of Bangladesh. PLoS ONE. 2022;17:e0269634. https://doi.org/10.1371/JOURNAL.PONE.0269634.
Google Scholar
Kroenke K, Spitzer RL, Williams JBW. The PHQ-9. J Gen Intern Med. 2001;16:606–13. https://doi.org/10.1046/J.1525-1497.2001.016009606.X.
Google Scholar
Cover TM, Hart PE. Nearest neighbor pattern classification. IEEE Trans Inf Theory. 1967;13:21–7. https://doi.org/10.1109/TIT.1967.1053964.
Google Scholar
Wu Y, Ianakiev K, Govindaraju V. Improved k-nearest neighbor classification. Pattern Recognit. 2002;35:2311–8. https://doi.org/10.1016/S0031-3203(01)00132-7.
Google Scholar
Breiman L. Random Forests. Mach Learn. 2001;45:5–32. https://doi.org/10.1023/A:1010933404324
Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–97. https://doi.org/10.1007/BF00994018
Friedman JH. Greedy function approximation: a gradient boostingmachine. Ann Stat. 2001;29:1189–232. https://doi.org/10.1214/AOS/1013203451
Chen T, Guestrin C. XGBoost: A scalable tree boosting system. Proc ACM SIGKDD Int Conf Knowl Discovery Data Min. 2016;13–17–August–2016:785–94. https://doi.org/10.1145/2939672.2939785.
Google Scholar
Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Stat. 2001;29:1189–232. https://doi.org/10.1214/AOS/1013203451.
Google Scholar
Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A. CatBoost: unbiased boosting with categorical features. Adv Neural Inf Process Syst. 2018;31. Available: https://github.com/catboost/catboost
Dorogush AV, Ershov V, Gulin A. CatBoost: gradient boosting with categorical features support. 2018 [cited 17 Nov 2024]. Available: https://arxiv.org/abs/1810.11363v1
Matarazzo BB, Eagan A, Landes SJ, Mina LK, Clark K, Gerard GR, et al. The veterans health administration REACH VET program: suicide predictive modeling in practice. Psychiatr Serv. 2023;74:206–9. https://doi.org/10.1176/APPI.PS.202100629.
Google Scholar
García-Lara RA, Suleiman-Martos N, Membrive-Jiménez MJ, García-Morales V, Quesada-Caballero M, Guisado-Requena IM, et al. Prevalence of depression and related factors among patients with chronic disease during the COVID-19 pandemic: A systematic review and Meta-Analysis. Diagnostics. 2022;12:3094. https://doi.org/10.3390/diagnostics12123094.
Google Scholar
Barberio B, Zamani M, Black CJ, Savarino EV, Ford AC. Prevalence of symptoms of anxiety and depression in patients with inflammatory bowel disease: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2021;6:359–70. https://doi.org/10.1016/S2468-1253(21)00014-5.
Google Scholar
Adejumo OA, Edeki IR, Sunday Oyedepo D, Falade J, Yisau OE, Ige OO, et al. Global prevalence of depression in chronic kidney disease: a systematic review and meta-analysis. J Nephrol. 2024;37:2455–72. https://doi.org/10.1007/S40620-024-01998-5.
Google Scholar
Pilevarzadeh M, Amirshahi M, Afsargharehbagh R, Rafiemanesh H, Hashemi SM, Balouchi A. Global prevalence of depression among breast cancer patients: a systematic review and meta-analysis. Breast Cancer Res Treat. 2019;176:519–33. https://doi.org/10.1007/S10549-019-05271-3.
Google Scholar
Vidyasagaran AL, McDaid D, Faisal MR, Nasir M, Muliyala KP, Thekkumkara S, et al. Prevalence of mental disorders in South Asia: a systematic review of reviews. Cambridge Prisms: Global Mental Health. 2023;10: e78. https://doi.org/10.1017/GMH.2023.72
Rivera-Matos L, Andrews S, Eswaran S. Sociodemographic risk factors for depression in patients with chronic liver disease. Clin Liver Dis (Hoboken). 2022;20:38–42. https://doi.org/10.1002/CLD.1208.
Google Scholar
O’Donnell AT, Corrigan F, Gallagher S. The impact of anticipated stigma on psychological and physical health problems in the unemployed group. Front Psychol. 2015;6:147365. https://doi.org/10.3389/FPSYG.2015.01263.
Google Scholar
Roy T, Lloyd CE, Parvin M, Mohiuddin KGB, Rahman M. Prevalence of co-morbid depression in out-patients with type 2 diabetes mellitus in Bangladesh. BMC Psychiatry. 2012;12:1–10. https://doi.org/10.1186/1471-244X-12-123.
Google Scholar
Purtle J, Nelson KL, Yang Y, Langellier B, Stankov I, Diez Roux AV. Urban–Rural differences in older adult depression: A systematic review and Meta-analysis of comparative studies. Am J Prev Med. 2019;56:603–13. https://doi.org/10.1016/J.AMEPRE.2018.11.008.
Google Scholar
Ventriglio A, Torales J, Castaldelli-Maia JM, De Berardis D, Bhugra D. Urbanization and emerging mental health issues. CNS Spectr. 2021;26:43–50. https://doi.org/10.1017/S1092852920001236.
Google Scholar
De Boer N, Vermeulen J, Lin B, Van Os J, Ten Have M, De Graaf R, et al. Longitudinal associations between alcohol use, smoking, genetic risk scoring and symptoms of depression in the general population: a prospective 6-year cohort study. Psychol Med. 2023;53:1409–17. https://doi.org/10.1017/S0033291721002968.
Google Scholar
Nagy E, Tharwat S, Elsayed AM, Shabaka SAEG, Nassar MK. Anxiety and depression in maintenance Hemodialysis patients: prevalence and their effects on health-related quality of life. Int Urol Nephrol. 2023;55:2905–14. https://doi.org/10.1007/S11255-023-03556-7.
Google Scholar
Hahad O, Beutel M, Gilan DA, Michal M, Schulz A, Pfeiffer N, et al. The association of smoking and smoking cessation with prevalent and incident symptoms of depression, anxiety, and sleep disturbance in the general population. J Affect Disord. 2022;313:100–9. https://doi.org/10.1016/J.JAD.2022.06.083.
Google Scholar
Wu Z, Yue Q, Zhao Z, Wen J, Tang L, Zhong Z, et al. A cross-sectional study of smoking and depression among US adults: NHANES (2005–2018). Front Public Health. 2023;11:1081706. https://doi.org/10.3389/FPUBH.2023.1081706.
Google Scholar
Hu Z, Cui E, Chen B, Zhang M. Association between cigarette smoking and the risk of major psychiatric disorders: a systematic review and meta-analysis in depression, schizophrenia, and bipolar disorder. Front Med (Lausanne). 2025;12:1529191. https://doi.org/10.3389/FMED.2025.1529191.
Google Scholar
Li Y, Zhang C, Ding S, Li J, Li L, Kang Y, et al. Physical activity, smoking, alcohol consumption and depressive symptoms among young, early mature and late mature people: A cross-sectional study of 76,223 in China. J Affect Disord. 2022;299:60–6. https://doi.org/10.1016/J.JAD.2021.11.054.
Google Scholar
Balfour DJK, Ridley DL. The effects of nicotine on neural pathways implicated in depression: A factor in nicotine addiction? Pharmacol Biochem Behav. 2000;66:79–85. https://doi.org/10.1016/S0091-3057(00)00205-7.
Google Scholar
Kiran T, Halder P, Sharma D, Mehra A, Goel K, Behera A. Distribution and association of depression with tobacco consumption among middle-aged and elderly Indian population: nested multilevel modelling analysis of nationally representative cross-sectional survey. J Health Popul Nutr. 2025;44:1–19. https://doi.org/10.1186/S41043-025-00753-1.
Google Scholar
Pengpid S, Peltzer K. Tobacco use and incident sleep parameters among a rural ageing population in South Africa. Tob Induc Dis. 2023;21:02. https://doi.org/10.18332/TID/156844.
Google Scholar
Stanton R, To QG, Khalesi S, Williams SL, Alley SJ, Thwaite TL, et al. Depression, anxiety and stress during COVID-19: associations with changes in physical Activity, Sleep, tobacco and alcohol use in Australian adults. Int J Environ Res Public Health 2020. 2020;17:17: 4065. https://doi.org/10.3390/IJERPH17114065.
Google Scholar
McGovern MP, Dunn J, Bonnell LN, Leibowitz G, Waddell E, Rose G, et al. The association between depression and substance use among primary care patients with comorbid medical and behavioral health conditions. J Prim Care Community Health. 2023;14:21501319231200304. https://doi.org/10.1177/21501319231200302.
Google Scholar
Schuch FB, Vancampfort D, Firth J, Rosenbaum S, Ward PB, Silva ES, et al. Physical activity and incident depression: A meta-analysis of prospective cohort studies. Am J Psychiatry. 2018;175:631–48. https://doi.org/10.1176/APPI.AJP.2018.17111194.
Google Scholar
Kandola A, Ashdown-Franks G, Hendrikse J, Sabiston CM, Stubbs B. Physical activity and depression: towards Understanding the antidepressant mechanisms of physical activity. Neurosci Biobehav Rev. 2019;107:525–39. https://doi.org/10.1016/J.NEUBIOREV.2019.09.040.
Google Scholar
Li Y, Wu Y, Zhai L, Wang T, Sun Y, Zhang D. Longitudinal association of sleep duration with depressive symptoms among Middle-aged and older Chinese. Sci Rep. 2017;2017 7:1. https://doi.org/10.1038/s41598-017-12182-0.
Google Scholar
Li Y, Sahakian BJ, Kang J, Langley C, Zhang W, Xie C, et al. The brain structure and genetic mechanisms underlying the nonlinear association between sleep duration, cognition and mental health. Nat Aging 2022. 2022;2:5. https://doi.org/10.1038/s43587-022-00210-2.
Google Scholar
Khandaker GM, Zuber V, Rees JMB, Carvalho L, Mason AM, Foley CN, et al. Shared mechanisms between coronary heart disease and depression: findings from a large UK general population-based cohort. Mol Psychiatry 2019. 2019;25:7. https://doi.org/10.1038/s41380-019-0395-3.
Google Scholar
Klap R, Unroe KT, Unützer J. Caring for mental illness in the united states: A focus on older adults. Am J Geriatric Psychiatry. 2003;11:517–24. https://doi.org/10.1097/00019442-200309000-00006.
Google Scholar
Walther L, Vogelsang F, Thom J, Hölling H, Grobe TG, Frerk T, et al. Assessing perceived need for mental healthcare among adults in Germany. Int J Public Health. 2025;70:1607927. https://doi.org/10.3389/IJPH.2025.1607927.
Google Scholar
Eimontas J, Gegieckaitė G, Zamalijeva O, Pakalniškienė V. Unmet healthcare needs predict depression symptoms among older adults. Int J Environ Res Public Health. 2022;19:8892. https://doi.org/10.3390/IJERPH19158892.
Google Scholar
Lee JY, Won D, Lee K. 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. PLoS ONE. 2023;18:e0288648. https://doi.org/10.1371/JOURNAL.PONE.0288648.
Google Scholar
Zheng Y, Zhang T, Yang S, Wang F, Zhang L, Liu Y. Using machine learning to predict the probability of incident 2-year depression in older adults with chronic diseases: a retrospective cohort study. BMC Psychiatry. 2024;24. https://doi.org/10.1186/S12888-024-06299-6
Husile H, Bao Q, Sarula S, La C, Wujisiguleng W, Siqintu S, et al. Accuracy of machine learning in predicting post-stroke depression: a systematic review and meta-analysis. Brain Behav. 2025;15. https://doi.org/10.1002/BRB3.70557
Zhao X, Wang Y, Li J, Liu W, Yang Y, Qiao Y, et al. A machine-learning-derived online prediction model for depression risk in COPD patients: A retrospective cohort study from CHARLS. J Affect Disord. 2025;377:284–93. https://doi.org/10.1016/j.jad.2025.02.063.
Google Scholar
Westreich D, Greenland S. The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am J Epidemiol. 2013;177:292–8. https://doi.org/10.1093/AJE/KWS412.
Google Scholar
Das P, Arif M, Hasan ME, ALmerab MM, Habib AA, Al Mamun F, et al. Prevalence and factors associated with insomnia amongchronic disease patients in Bangladesh: a machine learning study. Nat Sci Sleep. 2025;17:2541–67.
link