{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T01:23:24Z","timestamp":1777944204889,"version":"3.51.4"},"reference-count":46,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Public Health Research"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Conducting surveys on domestic violence across diverse countries, particularly in lower-middle-income nations like Nepal, poses significant challenges in understanding and addressing the multifaceted dynamics involved in domestic violence research. However, integrating machine learning can help uncover patterns and predictive factors. Therefore, this study aimed to evaluate and compare machine-learning models to identify population-level risk patterns of domestic violence associated with male demographic characteristics using nationally representative data from Nepal.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodology<\/jats:title>\n                    <jats:p>We utilized nationally representative data from the Nepal Demographic and Health Surveys (DHS) conducted in 2016 and 2022. A total of 7,813 observations were analyzed. The outcome variable captured whether women reported experiencing any form of physical or sexual violence. Data preprocessing and analysis were conducted using Stata and Python, with machine learning models implemented through the PyCaret framework. Multiple algorithms were evaluated based on performance metrics including accuracy, precision, recall, F1-score, and AUC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result<\/jats:title>\n                    <jats:p>Significant demographic shifts were observed between 2016 and 2022, including an increase in husbands with only primary education (from 23.2% to 42.52%) and rising rates of alcohol consumption. Among all models tested, LDA achieved the highest accuracy (74.61%) and F1-score (0.6924), while CatBoost and AdaBoost also showed competitive performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates the potential of machine learning models in predicting DV risk using male demographic profiles. While acknowledging that findings derived from Nepal-specific data may not be directly generalizable to other sociocultural settings, the findings highlight critical socio-economic determinants such as education, wealth, and substance use and support the use of predictive modeling as a complementary tool for early identification and targeted intervention.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/22799036261448793","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T09:35:14Z","timestamp":1777541714000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Domestic violence in Nepal: Insights from machine learning-based prediction"],"prefix":"10.1177","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6883-0284","authenticated-orcid":false,"given":"MD Nahid Hassan","family":"Nishan","sequence":"first","affiliation":[{"name":"North South University"},{"name":"Projahnmo Research Foundation"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M Z E M Naser Uddin","family":"Ahmed","sequence":"additional","affiliation":[{"name":"North South University"},{"name":"Projahnmo Research Foundation"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.4081\/hpr.2014.1821"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2020.e05334"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amepre.2019.08.035"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.avb.2024.101929"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40653-018-0230-z"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1177\/1077801298004003002"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.avb.2023.101835"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1108\/9781839828485"},{"key":"e_1_3_6_10_2","doi-asserted-by":"publisher","DOI":"10.1177\/10547738211027223"},{"key":"e_1_3_6_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/socsci12030117"},{"key":"e_1_3_6_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.socscimed.2015.10.021"},{"key":"e_1_3_6_13_2","unstructured":"kiener-manu katharina. Cybercrime Module 12 Key Issues: Gender-Based Interpersonal Cybercrime. https:\/\/www.unodc.org\/ (accessed 21 January 2024)."},{"key":"e_1_3_6_14_2","doi-asserted-by":"publisher","DOI":"10.1177\/15248380211038690"},{"key":"e_1_3_6_15_2","volume-title":"WHO Multi Country Study on Women\u2019s Health and Domestic Violence Against Women. Initial results on prevalence, health outcomes and women\u2019s responses","author":"Garc\u00eda-Moreno C","year":"2005","unstructured":"Garc\u00eda-Moreno C, Jansen H, Ellsberg M, et al.WHO Multi Country Study on Women\u2019s Health and Domestic Violence Against Women. Initial results on prevalence, health outcomes and women\u2019s responses, 2005."},{"key":"e_1_3_6_16_2","doi-asserted-by":"publisher","DOI":"10.1080\/16549716.2020.1788260"},{"key":"e_1_3_6_17_2","doi-asserted-by":"publisher","DOI":"10.1177\/0886260513505907"},{"key":"e_1_3_6_18_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0207091"},{"key":"e_1_3_6_19_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-022-13137-5"},{"key":"e_1_3_6_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10896-011-9382-4"},{"key":"e_1_3_6_21_2","doi-asserted-by":"publisher","DOI":"10.1177\/17455057241310289"},{"key":"e_1_3_6_22_2","doi-asserted-by":"publisher","DOI":"10.5468\/ogs.20211"},{"key":"e_1_3_6_23_2","doi-asserted-by":"publisher","DOI":"10.1080\/1359432X.2019.1635584"},{"key":"e_1_3_6_24_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph19095589"},{"key":"e_1_3_6_25_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2458-12-467"},{"key":"e_1_3_6_26_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0206101"},{"key":"e_1_3_6_27_2","unstructured":"Pradhan RP. The Ain of 1854 and after: Legal pluralism models of society and ethnicity in Nepal. Epub ahead of print 2015. https:\/\/doi.org\/10.13140\/RG.2.1.2039.7529"},{"key":"e_1_3_6_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoforum.2016.07.006"},{"key":"e_1_3_6_29_2","doi-asserted-by":"publisher","DOI":"10.1177\/15248380231222230"},{"key":"e_1_3_6_30_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph20064984"},{"key":"e_1_3_6_31_2","article-title":"What is Machine Learning and How Does It Work? In-Depth Guide","author":"Tucci L","unstructured":"Tucci L, Burns E. What is Machine Learning and How Does It Work? In-Depth Guide. Enterprise AI. https:\/\/www.techtarget.com\/searchenterpriseai\/definition\/machine-learning-ML (accessed 21 January 2024).","journal-title":"Enterprise AI"},{"key":"e_1_3_6_32_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph20064984"},{"key":"e_1_3_6_33_2","doi-asserted-by":"publisher","DOI":"10.1093\/police\/paae092"},{"key":"e_1_3_6_34_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2022.1015914"},{"key":"e_1_3_6_35_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5624475"},{"key":"e_1_3_6_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.array.2025.100393"},{"key":"e_1_3_6_37_2","volume-title":"World Bank Data Help Desk","author":"World Bank Country and Lending Groups","unstructured":"World Bank Country and Lending Groups. World Bank Data Help Desk. https:\/\/datahelpdesk.worldbank.org\/knowledgebase\/articles\/906519-world-bank-country-and-lending-groups (accessed 5 January 2024)."},{"key":"e_1_3_6_38_2","volume-title":"Nepal Demographic and Health Survey 2016","author":"Ministry of Health - MOH\/Nepal, New ERA\/Nepal, ICF","year":"2017","unstructured":"Ministry of Health - MOH\/Nepal, New ERA\/Nepal, ICF. Nepal Demographic and Health Survey 2016. MOH\/Nepal, New ERA, and ICF, 2017. https:\/\/dhsprogram.com\/pubs\/pdf\/FR336\/FR336.pdf"},{"key":"e_1_3_6_39_2","volume-title":"Nepal Demographic and Health Survey 2022","author":"Ministry of Health and Population [Nepal], ICF","year":"2023","unstructured":"Ministry of Health and Population [Nepal], ICF. Nepal Demographic and Health Survey 2022. Ministry of Health and Population [Nepal], 2023. https:\/\/www.dhsprogram.com\/pubs\/pdf\/FR379\/FR379.pdf"},{"key":"e_1_3_6_40_2","unstructured":"The DHS Program - Demographic and Health Survey (DHS). https:\/\/dhsprogram.com\/Methodology\/Survey-Types\/DHS.cfm (accessed 21 January 2024)."},{"key":"e_1_3_6_41_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1125056"},{"key":"e_1_3_6_42_2","doi-asserted-by":"publisher","DOI":"10.1177\/08862605211016337"},{"key":"e_1_3_6_43_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-020-10021-y"},{"key":"e_1_3_6_44_2","doi-asserted-by":"publisher","DOI":"10.6017\/ijahe.v9i1.15229"},{"key":"e_1_3_6_45_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-022-14389-x"},{"key":"e_1_3_6_46_2","doi-asserted-by":"publisher","DOI":"10.2147\/PI.S12431"},{"key":"e_1_3_6_47_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12954-025-01301-0"}],"container-title":["Journal of Public Health Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/22799036261448793","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/22799036261448793","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/22799036261448793","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:50:12Z","timestamp":1777708212000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/22799036261448793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":46,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["10.1177\/22799036261448793"],"URL":"https:\/\/doi.org\/10.1177\/22799036261448793","relation":{},"ISSN":["2279-9036","2279-9036"],"issn-type":[{"value":"2279-9036","type":"print"},{"value":"2279-9036","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"article-number":"22799036261448793"}}