Big Data Analytics for Women’s Healthcare: Insights from Urban and Rural Pathological Data
Adishree A. Kulkarni
Adishree A. Kulkarni, Student, Department of Science, Rajarshi Chhatrapati Shahu Junior College, Nashik (Maharashtra), India.
Manuscript received on 05 November 2025 | First Revised Manuscript received on 25 November 2025 | Second Revised Manuscript received on 18 December 2025 | Manuscript Accepted on 15 January 2026 | Manuscript published on 30 January 2026 | PP: 10-14 | Volume-6 Issue-2 January 2026 | Retrieval Number: 100.1/ijpmh.B113506020126 | DOI: 10.54105/ijpmh.B1135.06020126
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The fast introduction of big data analytics to healthcare is transforming medical research and clinical practices, especially in the area of women’s health. Women in urban and rural areas have unique health needs due to differences in lifestyle, environment, socioeconomic status, and access to health care. The paper aims to discuss pathological samples from women across various environments to determine the differences and commonalities in their health status. Combining electronic health record (EHR) data, imaging data, genomic data, wearable data, and socio-economic data, we will discover patterns that modulate disease risk, disease progression, and drug response. Lifestyle diseases and cancers are common in urban regions as a result of stress and exposure to the environment, and rural women face the challenges of limited access to healthcare, late diagnosis, and poor health literacy. Early disease detection, improved diagnostics, and tailored treatment are among the prospects of applying machine learning and predictive models to large datasets. Nevertheless, some tasks, such as data privacy, inadequate infrastructure in rural areas, and ethics, are essential. This paper demonstrates the potential of big data analytics to close healthcare disparities, facilitate access to justice, and support personalised medicine for women across diverse groups.
Keywords: Big Data Analytics, Women’s Healthcare, Pathological Data, Urban and Rural Health, Healthcare Disparities, Predictive Analytics, Machine Learning.
Scope of the Article: Public Health