{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:54:22Z","timestamp":1777503262396,"version":"3.51.4"},"reference-count":45,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":["Cancer Inform"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Post-mastectomy PTSD is a serious mental health issue, but it has not been studied enough, particularly in low-resource settings like Bangladesh. This study aimed to predict PTSD among breast cancer survivors using machine learning (ML) models and identify significant predictors through the Boruta algorithm, a feature selection tool, offering scalable solutions for early detection and intervention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>A cross-sectional study of 138 post-mastectomy breast cancer patients was conducted across 3 hospitals in Bangladesh. Data on sociodemographic, health history, social experience, and treatment were collected using validated tools, including the PTSD Checklist for DSM-5 (PCL-5). The Boruta algorithm identified key predictors, and 10\u2009ML models were evaluated for PTSD prediction using metrics such as accuracy, sensitivity, specificity, and AUC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Random Forest (RF) outperformed other models (accuracy: 88.9%, AUC: 0.914). Significant predictors included education, monthly income, and changes in family behaviour. Factors like marital status, having chronic diseases, and hormone therapy were not statistically significant. PTSD prevalence was 34.1%, with urban residents and younger patients facing higher risks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>ML models, particularly RF, demonstrated strong predictive performance and identified critical PTSD predictors. These findings highlight the potential for cost-effective PTSD screening in resource-constrained settings. Future research should focus on broader validation and longitudinal studies to refine predictive models.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/11769351251401330","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T07:25:23Z","timestamp":1765610723000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction and Feature Selection of Mastectomy-Related Post Traumatic Stress Disorder (PTSD) Using Machine Learning Among Breast Cancer Patients in Bangladesh"],"prefix":"10.1177","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4903-3690","authenticated-orcid":false,"given":"Syed Billal","family":"Hossain","sequence":"first","affiliation":[{"name":"Department of Public Health, University of Science and Technology Chittagong (USTC), Chattogram, Bangladesh"},{"name":"Department of Public Health, Daffodil International University (DIU), Dhaka, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md. Mizanoor","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Statistics, Mawlana Bhashani Science and Technology University (MBSTU), Tangail, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7556-5102","authenticated-orcid":false,"given":"Kapashia Binte","family":"Giash","sequence":"additional","affiliation":[{"name":"Department of Statistics, Mawlana Bhashani Science and Technology University (MBSTU), Tangail, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2054-8434","authenticated-orcid":false,"given":"Md. Hazrat","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Statistics, Mawlana Bhashani Science and Technology University (MBSTU), Tangail, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mst. Asma","family":"Akter","sequence":"additional","affiliation":[{"name":"Department of Public Health, Daffodil International University (DIU), Dhaka, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.B.M. Alauddin","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Public Health, Daffodil International University (DIU), Dhaka, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,12,12]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2024.1429647"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.2147\/NSS.S166089"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1186\/2193-1801-3-392"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.32934"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.21037\/abs-23-33"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pcorm.2024.100401"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.4040\/jkan.22120"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apnu.2023.01.003"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejtd.2025.100535"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajp.2017.10.029"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.maturitas.2015.11.002"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.5498\/wjp.v14.i1.8"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.3332\/ecancer.2019.917"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1192\/bji.2021.41"},{"issue":"3","key":"e_1_3_3_17_2","first-page":"132","article-title":"Sociodemographic factors and clinical presentation of women attending Cancer Detection Centre, Kolkata for breast examination","volume":"5","author":"Sarkar S","year":"2020","unstructured":"Sarkar S, Ghosh D, Mahata S, et al. Sociodemographic factors and clinical presentation of women attending Cancer Detection Centre, Kolkata for breast examination. J Clin Transl Res. 2020;5(3):132-139.","journal-title":"J Clin Transl Res"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-92137-y"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1186\/s43045-023-00375-5"},{"issue":"3","key":"e_1_3_3_20_2","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw A","year":"2002","unstructured":"Liaw A, Wiener M. Classification and Regression by randomForest. R News 2002;2(3):18-22.","journal-title":"R News"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022627411411"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejtd.2025.100502"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12920-024-02002-6"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1200\/CCI.21.00116"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v036.i11"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2015.05.057"},{"issue":"12","key":"e_1_3_3_27_2","first-page":"1533","article-title":"Prevalence of posttraumatic stress disorder among breast cancer patients: a meta-analysis","volume":"45","author":"Wu X","year":"2016","unstructured":"Wu X, Wang J, Cofie R, Kaminga AC, Liu A. Prevalence of posttraumatic stress disorder among breast cancer patients: a meta-analysis. Iran J Public Health. 2016;45(12):1533-1544.","journal-title":"Iran J Public Health"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.oor.2024.100662"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1002\/jts.22059"},{"key":"e_1_3_3_30_2","unstructured":"American Psychiatric Association. DSM-5-TR Update. 2025. https:\/\/www.psychiatry.org\/getmedia\/b68a5776-f88c-45c7-9535-fd219d7aa5cb\/APA-DSM5TR-Update-September-2025.pdf"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11165-016-9602-2"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.3233\/FI-2010-288"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1814259"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.2196\/17119"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1353\/hpu.2016.0093"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2025.1447232"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2018.00040"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0286366"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1600-0447.2009.01438.x"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2215-0366(21)00395-3"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychores.2018.09.008"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1002\/pon.5252"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1002\/jts.22282"},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1037\/fam0000363"},{"key":"e_1_3_3_46_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-62713-5"}],"container-title":["Cancer Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/11769351251401330","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/11769351251401330","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/11769351251401330","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:20:53Z","timestamp":1777389653000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/11769351251401330"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":45,"alternative-id":["10.1177\/11769351251401330"],"URL":"https:\/\/doi.org\/10.1177\/11769351251401330","relation":{},"ISSN":["1176-9351","1176-9351"],"issn-type":[{"value":"1176-9351","type":"print"},{"value":"1176-9351","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"article-number":"11769351251401330"}}