Early Gestational Diabetes Detection Using Neural Network

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 not cost effective and usually 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.

Appropriate Job Selection Using Machine Learning Techniques

“…Additionally, research is carried out to choose appropriate jobs using various techniques, including Decision Trees, Artificial Neural Networks, Random Forests, Adaboost, and SVM [24][25][26]. To satisfy industry selection criteria, Awujoola et al propose a current, accurate, and deserving machine learning classification model that can be deployed, implemented, and utilized to predict and evaluate job candidate qualities based on their academic performance records [27].…”

PLG-007 and Its Active Component Galactomannan-α Competitively Inhibit Enzymes That Hydrolyze Glucose Polymers

“…Diabetic patients require hypoglycemic drugs, such as insulin, to adequately control their blood sugar levels to reduce hyperglycemia associated with the ingestion of carbohydrates or food with a high glycemic index. Screening and diagnostic tests have been used to assess high levels of glucose in plasma and serum; e.g., Hera et al [ 7 ] developed an approach to detect gestational diabetes mellitus using different neural network models and patient data. However, the efficacy of the diabetes management program is often limited due to non-compliance and poor uptake of systemic drugs, as well as the safety concern associated with systemic drug exposure.…”