iosr-JNHS   Volume-14 ~ Issue-5 ~ Sep. – Oct. 2025

[1]. Oliphant SS, Jones KA, Wang L, Et Al. Trends Over Time With Commonly Performed Obstetric And Gynecologic Inpatient Procedures. Obstet Gynecol 2010;116(4):926–931. DOI: 10.1097/AOG.0b013e3181f38599.
[2]. Bhatia M,Banerjee K, Dixit P. A Cross-Sectional Study On Assessment Of Variation In Caesarean Delivery Rates Between Public And Private Health Facilities In India From 2005 To 2016. National Library Of Medicine. 2020;3(8):E2015022.
[3]. O′Sullivan JF. Caesarean Birth. Ulster Med J 1990;59(1):1–10.
[4]. Lanneau GS, Muffley P, Magann EF. Chapter 74. Caesarean Births: Surgical Techniques, Vol. 2. In: Gynecology And Obstetrics. Lippincott; 2004.
[5]. Vallejos Parás A, Espino S, Jaimes Betancourt L, Et Al. Obstetrician’s Attitudes About Delivery Through Caesarean Section: A Study In Hospitals At Mexico City. Perinatol Reprod Hum 2018;32(1):19–26. DOI: 10.1016/J.Rprh.2018.03.012.

[1]. Manish P.A Assessment Of Knowledge Among Pregnant Women Regarding Anemia Asian Journal Of Nursing Education And Research 2020; 10(2). Available From – Https:// Ajmer –Com> Abstract View. DOI - 10.5985/2349 – 2996 .2020, 00036.1.
[2]. WHO, Hemoglobin Concentration For The Diagnosis Of Anemia And Assessment Of Survey Vitamin And Mineral Nutrition Information System; 2011. [Google Scholar]
[3]. WHO Prevalence Of Anemia Worldwide 1993 -2005. World Health Organization; 2008. [Google Scholar]
[4]. Jacob A. A Comprehensive Textbook Of Midwifery & Gyneocological Nursing. 5th Ed. Jaypee Brothers Medical Publishers, New Delhi, 2019. Pp-271-73
[5]. Mahalakshmi T, G. Bhuvaneswari, A Study To Assess The Knowledge Of Anemia Among Antenatal Mothers And To Develop An Informational Pamphlet On Anemia, At Urban Health Centre Saidapet, International Journal Of Multidisciplinary Educational Research 2014;3(3). Available From- Https://Www.Researchgate.Net/Publication/344713931.

[1]. Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial Intelligence With Multi-Functional Machine Learning Platform Development For Better Healthcare And Precision Medicine. Database, 2020, Baaa010. Https://Doi.Org/10.1093/Database/Baaa010
[2]. Al-Zaiti, S. S., Besomi, L., Bouzid, Z., Faramand, Z., Frisch, S., Martin-Gill, C., Gregg, R., Saba, S., Callaway, C., & Sejdic, E. (2020). Machine Learning-Based Prediction Of Acute Coronary Syndrome Using Only The Pre-Hospital 12-Lead Electrocardiogram. Nature Communications, 11, 3966. Https://Doi.Org/10.1038/S41467-020-17804-2
[3]. Alghatani, K., Ammar, N., Rezgui, A., & Shaban, K. (2022). Predicting Intensive Care Unit Length Of Stay And Mortality Using Patient Vital Signs: Machine Learning Model Development And Validation. JMIR Medical Informatics, 10(5), E32593. Https://Doi.Org/10.2196/32593
[4]. Akinbode, A. K., Olinmah, F. I., Chima, O. K., Okare, B. P., &Adeloju, T. D. Using Business Intelligence Tools To Monitor Chronic Disease Trends Across Demographics. International Journal Of Scientific Research In Computer Science, Engineering And Information Technology. 2024a, 10(4), 739-776
[5]. Akinbode, A. K., Olinmah, F. I., Chima, O. K., Okare, B. P., &Adeloju, T. D. A KPI Optimization Framework For Institutional Performance Using R And Business Intelligence Tools. Gyanshauryam, International Scientific Refereed Research Journal. 2023, 6(5), 274-308