Big Data Analytics for Women’s Healthcare: Insights from Urban and Rural Pathological Data
Article Sidebar
Main Article Content
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.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
R. A. Bang, M. Baitule, S. Sarmukaddam, A. T. Bang, Y. Choudhary, and O. Tale, “Community health: High prevalence of gynaecological diseases in rural Indian women,” Gadchiroli, India. DOI: https://doi.org/10.1016/s0140-6736(89)91438-4
M. Szubert, M. Ilowiecka, J. Wilczynski, P. Bilinski, and C. Wojtyla, “Health-related behaviours of pregnant women residing in urban and rural areas in Poland,” Int. J. Environ. Res. Public Health, vol. 17, no. 12, p. 4395, 2020, DOI: http://doi.org/10.3390/ijerph17124395.
N. Jaysawal and S. Saha, “Impact of women’s education on their health conditions—An overview,” Int. J. Adv. Multidiscip. Res., vol. 9, no. 9, 2022, DOI: https://doi.org/10.22192/ijamr.2022.09.09.007
S. S. Parvati, S. Tiwari, K. Gupta, and K. Saroj, “A study on health and nutritional problems of women in rural community,” Int. J. Sci. Dev. Res., vol. 7, no. 4, Apr. 2022. DOI: https://doi.org/10.1729/Journal.29852,
K. Shailaja and M. Jabbar, “Prediction of breast cancer using big data analytics,” Int. J. Eng. Technol., vol.. 7, no. 4.6, pp. 223–226, 2018, DOI: http://doi.org/10.14419/ijet.v7i4.6.20480.
M. Mehmood, M. Rizwan, M. Gregus, and S. Abbas, “Machine learning assisted cervical cancer detection,” Front. Public Health, vol. 9, Dec. 2021, DOI: http://doi.org/10.3389/fpubh.%202021.788376.
M. Rahimi, A. Akbari, F. Asadi, et al., “Cervical cancer survival prediction by machine learning algorithms: A systematic review,” BMC Cancer, vol. 23, no. 341, 2023. DOI: http://doi.org/10.1186/s12885-023-10808-3.