{
  "abstract": "Postpartum depression (PPD) is currently a serious mental disorder that occurs after childbirth. It affects the well-being of both the mother and the child. However, early screening is still limited in low- and middle-income countries like Bangladesh due to socio-economic challenges and social stigma. This paper presents a comprehensive and multidimensional dataset consisting of 766 participants to facilitate data-driven PPD research. The participating women were between the ages of 18–41 years. The data were collected through a cross-sectional study using face-to-face interviews and hospital records at LABAID Diagnostics, Pabna, Bangladesh, between March and June 2025. The dataset integrates 51 variables, including relevant factors such as sociodemographic factors, obstetric history, neonatal details, and psychosocial indicators. Maternal health was assessed using standardized screening tools: PHQ-2 and PHQ-9 for symptoms before and during pregnancy, and Edinburgh Postpartum Depression Scale (EPDS) for postpartum symptoms. Results indicated that the prevalence of depression during pregnancy among the participants was 40.47%. A key feature of this dataset is the clear reporting of class imbalance, which can be reused for statistical analysis, epidemiological studies, and validation of depression screening tools, making it a valuable resource for building robust machine learning (ML) and interpretable AI (XAI) models for early risk prediction. This study fills an important gap in publicly available, high-quality maternal health information in the South Asian context. It also provides the basis for evidence-based interventions and targeted support for at-risk mothers.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, American International University Bangladesh, 1229, Dhaka, Bangladesh",
    "Department of Electrical & Computer Engineering, North South University, 1229, Dhaka, Bangladesh",
    "Department of Computer Science & Engineering, East West University, 1212, Dhaka, Bangladesh",
    "Department of Computer Science & Engineering, Southeast University, 1208, Dhaka, Bangladesh",
    "Department of Computer Science & Engineering, University of Chittagong, 4331, Chittagong, Bangladesh",
    "Department of Computer Science and Software Engineering, Malardalen University, Vastmanland, Sweden"
  ],
  "article_type": "data paper",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shahriar Siddique Ayon"
    },
    {
      "affs": [
        1
      ],
      "name": "Shahidul Morsalin Jahin"
    },
    {
      "affs": [
        0
      ],
      "name": "Md. Ebrahim Hossain"
    },
    {
      "affs": [
        0
      ],
      "name": "Md. Saef Ullah Miah"
    },
    {
      "affs": [
        2
      ],
      "name": "Kazi Mostaq Hridoy"
    },
    {
      "affs": [
        3
      ],
      "name": "B.M. Mredul Arafin"
    },
    {
      "affs": [
        4
      ],
      "name": "Anik Dey Shaon"
    },
    {
      "affs": [
        5
      ],
      "name": "Md Rakibul Islam"
    },
    {
      "affs": [
        0
      ],
      "name": "M.F. Mridha"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/asgq76hjc3pqm.bib",
  "cited_by": 0,
  "doi": "10.1016/j.dib.2026.113210",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13733464/",
  "id": "asgq76hjc3pqm",
  "issns": [
    "2352-3409"
  ],
  "landing_url": "https://doi.org/10.1016/j.dib.2026.113210",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Data Article"
    ],
    [
      "openalex",
      "2739|Maternal Mental Health During Pregnancy and Postpartum"
    ]
  ],
  "openalex": "W7204687220",
  "pdf_url": null,
  "pmcid": "PMC13733464",
  "pmid": "42985228",
  "published": "2026-08-30",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/asgq76hjc3pqm.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T12:23:14Z",
      "native_id": "PMC13733464",
      "source": "pmc",
      "url": "https://doi.org/10.1016/j.dib.2026.113210"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2739|Maternal Mental Health During Pregnancy and Postpartum",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "A comprehensive dataset for postpartum depression prediction among bangladeshi mothers: Sociodemographic, obstetric, and psychosocial factors with class imbalance insights",
  "title_text": "A comprehensive dataset for postpartum depression prediction among bangladeshi mothers: Sociodemographic, obstetric, and psychosocial factors with class imbalance insights",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:23:14Z",
  "url": "https://research.draconitic.com/paper/asgq76hjc3pqm",
  "venue": "Data in Brief",
  "versions": []
}