EARLY GESTATIONAL DIABETES DETECTION THROUGH NEURAL NETWORK
| dc.contributor.author | TANZINA RAHMAN HERA | |
| dc.date.accessioned | 2025-11-23T08:35:08Z | |
| dc.date.available | 2025-11-23T08:35:08Z | |
| dc.date.issued | 2018-07-30 | |
| dc.description.abstract | Gestational Diabetes Mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. Fifty percent of GDM patients develop type 2 Diabetes in next twenty years and as well as the newborn can also be affected by diabetes in their lifetime. So the long term complications for both the mother and the child cannot be ignored. In view of maternal morbidity and mortality as well as fetal complications, early diagnosis is an utmost necessity in the present scenario. In developing country like Bangladesh, early detection and prevention is more cost effective and troublesome. So there is an urgent need for a well-designed method for the detection of gestational diabetes mellitus. The purpose of this study is to predict the GDM in the first trimester. This research presents and compares some Artificial Neural Network (ANN) models on the early detection of Gestational diabetes mellitus and chooses the best neural network model among them to detect GDM early. | |
| dc.identifier.uri | https://dspace.nstu.ac.bd/handle/123456789/181 | |
| dc.title | EARLY GESTATIONAL DIABETES DETECTION THROUGH NEURAL NETWORK |