PERI_DEP: A dataset of mother's mental health in Pakistan

Amna Zafar; Beenish Ayesha Akram; Muhammad Wasim; Maham; Ivan Miguel Pires; Paulo Jorge Coelho

doi:10.1016/j.dib.2025.111621

PERI_DEP: A dataset of mother's mental health in Pakistan

Data Brief. 2025 May 7:60:111621. doi: 10.1016/j.dib.2025.111621. eCollection 2025 Jun.

Authors

Amna Zafar¹, Beenish Ayesha Akram², Muhammad Wasim³, Maham¹, Ivan Miguel Pires⁴, Paulo Jorge Coelho^{5

6}

Affiliations

¹ Department of Computer Science, University of Engineering and Technology, Lahore, Pakistan.
² Department of Computer Engineering, University of Engineering and Technology, Lahore, Pakistan.
³ Department of Computer Science, University of Management and Technology, Lahore (Sialkot Campus), Pakistan.
⁴ Instituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, Águeda, Portugal.
⁵ School of Technology and Management, Polytechnic University of Leiria, Leiria, Portugal.
⁶ Institute for Systems Engineering and Computers at Coimbra, University of Coimbra, Coimbra, Portugal.

Abstract

Perinatal depression (PND) represents a multifaceted mental health issue that impacts women throughout the perinatal period. Existing datasets have a class imbalance issue, resulting in biased outcomes. In Pakistan, we developed a novel dataset called PERI_DEP. This dataset leverages the Patient Health Questionnaire (PHQ-9), Edinburgh Postnatal Depression Scale (EPDS), and socio-demographic questionnaires to gather information about mental health state and socioeconomic details of women participants in urban and rural areas. Our novel PERI_DEP dataset contains 14,008 samples, and women from Lahore and Gujranwala participated. To tackle the issue of class imbalance, we employed Generative Adversarial Network (GAN) oversampling technique on our data. A key insight derived from this dataset is the comparative socio-demographic divide of women in rural and urban areas of Pakistan. PERI_DEP dataset enhances hospital capabilities by streamlining screening processes, customizing interventions, and enabling researchers to identify risk factors and develop new treatments accurately.

Keywords: Depression; Mental health; Perinatal; Postnatal; Prenatal.