Analysis of Gestational Diabetes Mellitus (GDM) and Its Impact on Maternal and Fetal Health: A Comprehensive Dataset Study Using Data Analytic Tool Power BI

作     者：Shahistha Jabeen Hashim Arthur McAdams Shahistha Jabeen Hashim;Arthur McAdams

作者机构：Department of Technology Management University of Bridgeport Bridgeport USA 

出 版 物：《Journal of Data Analysis and Information Processing》 (数据分析和信息处理（英文）)

年 卷 期：2024年第12卷第2期

页      面：232-247页

学科分类：1002[医学-临床医学] 100211[医学-妇产科学] 10[医学] 

主　　题：Gestational Diabetes Visualization Data Analytics Data Modelling Pregnancy Power BI 

摘      要：Gestational Diabetes Mellitus (GDM) is a significant health concern affecting pregnant women worldwide. It is characterized by elevated blood sugar levels during pregnancy and poses risks to both maternal and fetal health. Maternal complications of GDM include an increased risk of developing type 2 diabetes later in life, as well as hypertension and preeclampsia during pregnancy. Fetal complications may include macrosomia (large birth weight), birth injuries, and an increased risk of developing metabolic disorders later in life. Understanding the demographics, risk factors, and biomarkers associated with GDM is crucial for effective management and prevention strategies. This research aims to address these aspects comprehensively through the analysis of a dataset comprising 600 pregnant women. By exploring the demographics of the dataset and employing data modeling techniques, the study seeks to identify key risk factors associated with GDM. Moreover, by analyzing various biomarkers, the research aims to gain insights into the physiological mechanisms underlying GDM and its implications for maternal and fetal health. The significance of this research lies in its potential to inform clinical practice and public health policies related to GDM. By identifying demographic patterns and risk factors, healthcare providers can better tailor screening and intervention strategies for pregnant women at risk of GDM. Additionally, insights into biomarkers associated with GDM may contribute to the development of novel diagnostic tools and therapeutic approaches. Ultimately, by enhancing our understanding of GDM, this research aims to improve maternal and fetal outcomes and reduce the burden of this condition on healthcare systems and society. However, it’s important to acknowledge the limitations of the dataset used in this study. Further research utilizing larger and more diverse datasets, perhaps employing advanced data analysis techniques such as Power BI, is warranted to corro