Maternal health risk factors dataset: Clinical parameters and insights from rural BangladeshMendeley Data
Pregnancy-related complications and their consequences pose significant public health challenges, particularly in rural and developing areas where healthcare resources are limited. Monitoring clinical parameters during pregnancy improves diagnosis, treatment, and maternal health prognosis. This data...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000952 |
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| author | Mayen Uddin Mojumdar Dhiman Sarker Md Assaduzzaman Hasin Arman Shifa Md. Anisul Haque Sajeeb Oahidul Islam Md Shadikul Bari Mohammad Jahangir Alam Narayan Ranjan Chakraborty |
| author_facet | Mayen Uddin Mojumdar Dhiman Sarker Md Assaduzzaman Hasin Arman Shifa Md. Anisul Haque Sajeeb Oahidul Islam Md Shadikul Bari Mohammad Jahangir Alam Narayan Ranjan Chakraborty |
| author_sort | Mayen Uddin Mojumdar |
| collection | DOAJ |
| description | Pregnancy-related complications and their consequences pose significant public health challenges, particularly in rural and developing areas where healthcare resources are limited. Monitoring clinical parameters during pregnancy improves diagnosis, treatment, and maternal health prognosis. This database includes records of pregnant patients from Kurigram General Hospital, Bangladesh. It captures core health parameters such as age, blood pressure (systolic and diastolic), blood sugar levels, body temperature, BMI, current mental health status, pre-existing medical history, gestational diabetes status, and heart rate. The diversity of data collected in this dataset is essential for understanding potential health changes associated with pregnancy. It will aid in generating high-risk pregnancy evaluation and prediction models to support clinical management. This dataset is valuable for its potential to serve as a benchmark for comparing maternal health responses across different clinical conditions of patients, thereby contributing to a broader understanding of pregnancy-related complications. The study's preprocessing methods, which included data cleaning, normalization, and encoding, ensured high-quality data for statistical analysis. Initial findings used statistical tests to explore associations within the data. A Chi-Square test analyzed the relationship between preexisting diabetes and risk levels, revealing a significant association with a p-value of 4.85e-119. A Z-test was also conducted to compare clinical parameters between pregnant patients with and without diabetes, with a sample ratio of 337:811. This test showed a significant difference in BMI (body mass index), with a p-value of 2.23e-24, indicating that preexisting diabetes impacts BMI. A T-test for BMI revealed a significant difference, with a p-value of 1.405e-20. These findings further elucidate how specific age and body mass index details influence the risk levels associated with maternal clinical conditions. In summary, this database will be highly valued and a significant asset for research studies on maternal health in pregnant patients, public health strategies, and the enhancing diagnostic and treatment modalities for patients. |
| format | Article |
| id | doaj-art-ccdbfd7642d44cdd82114278627e1fe1 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-ccdbfd7642d44cdd82114278627e1fe12025-02-08T05:00:35ZengElsevierData in Brief2352-34092025-04-0159111363Maternal health risk factors dataset: Clinical parameters and insights from rural BangladeshMendeley DataMayen Uddin Mojumdar0Dhiman Sarker1Md Assaduzzaman2Hasin Arman Shifa3Md. Anisul Haque Sajeeb4Oahidul Islam5Md Shadikul Bari6Mohammad Jahangir Alam7Narayan Ranjan Chakraborty8Department of CSE, Daffodil International University, Dhaka, BangladeshDepartment of CSE, Daffodil International University, Dhaka, BangladeshCorresponding author.; Department of CSE, Daffodil International University, Dhaka, BangladeshDepartment of CSE, Daffodil International University, Dhaka, BangladeshDepartment of CSE, Daffodil International University, Dhaka, BangladeshDepartment of CSE, Daffodil International University, Dhaka, BangladeshDepartment of CSE, Daffodil International University, Dhaka, BangladeshDepartment of CSE, Daffodil International University, Dhaka, BangladeshDepartment of CSE, Daffodil International University, Dhaka, BangladeshPregnancy-related complications and their consequences pose significant public health challenges, particularly in rural and developing areas where healthcare resources are limited. Monitoring clinical parameters during pregnancy improves diagnosis, treatment, and maternal health prognosis. This database includes records of pregnant patients from Kurigram General Hospital, Bangladesh. It captures core health parameters such as age, blood pressure (systolic and diastolic), blood sugar levels, body temperature, BMI, current mental health status, pre-existing medical history, gestational diabetes status, and heart rate. The diversity of data collected in this dataset is essential for understanding potential health changes associated with pregnancy. It will aid in generating high-risk pregnancy evaluation and prediction models to support clinical management. This dataset is valuable for its potential to serve as a benchmark for comparing maternal health responses across different clinical conditions of patients, thereby contributing to a broader understanding of pregnancy-related complications. The study's preprocessing methods, which included data cleaning, normalization, and encoding, ensured high-quality data for statistical analysis. Initial findings used statistical tests to explore associations within the data. A Chi-Square test analyzed the relationship between preexisting diabetes and risk levels, revealing a significant association with a p-value of 4.85e-119. A Z-test was also conducted to compare clinical parameters between pregnant patients with and without diabetes, with a sample ratio of 337:811. This test showed a significant difference in BMI (body mass index), with a p-value of 2.23e-24, indicating that preexisting diabetes impacts BMI. A T-test for BMI revealed a significant difference, with a p-value of 1.405e-20. These findings further elucidate how specific age and body mass index details influence the risk levels associated with maternal clinical conditions. In summary, this database will be highly valued and a significant asset for research studies on maternal health in pregnant patients, public health strategies, and the enhancing diagnostic and treatment modalities for patients.http://www.sciencedirect.com/science/article/pii/S2352340925000952Early signs of pregnancyMaternal healthMaternal health datasetMaternal factorsPregnancy dataset Bangladesh |
| spellingShingle | Mayen Uddin Mojumdar Dhiman Sarker Md Assaduzzaman Hasin Arman Shifa Md. Anisul Haque Sajeeb Oahidul Islam Md Shadikul Bari Mohammad Jahangir Alam Narayan Ranjan Chakraborty Maternal health risk factors dataset: Clinical parameters and insights from rural BangladeshMendeley Data Data in Brief Early signs of pregnancy Maternal health Maternal health dataset Maternal factors Pregnancy dataset Bangladesh |
| title | Maternal health risk factors dataset: Clinical parameters and insights from rural BangladeshMendeley Data |
| title_full | Maternal health risk factors dataset: Clinical parameters and insights from rural BangladeshMendeley Data |
| title_fullStr | Maternal health risk factors dataset: Clinical parameters and insights from rural BangladeshMendeley Data |
| title_full_unstemmed | Maternal health risk factors dataset: Clinical parameters and insights from rural BangladeshMendeley Data |
| title_short | Maternal health risk factors dataset: Clinical parameters and insights from rural BangladeshMendeley Data |
| title_sort | maternal health risk factors dataset clinical parameters and insights from rural bangladeshmendeley data |
| topic | Early signs of pregnancy Maternal health Maternal health dataset Maternal factors Pregnancy dataset Bangladesh |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000952 |
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