Modelling effective diagnosis of risk complications in gestational diabetes mellitus: an e-diabetic expert system for pregnant women

Sreedevi, E. P.; Lakshmi, K; Krishna, E Chaitanya; Padmavathamma, M.

doi:10.1117/12.956489

Cited by 5 publications

(1 citation statement)

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“…J.K. Olarinoye et al [3] compared the diagnostic performances of 75g and 100g Oral Glucose Tolerance Tests in detecting GDM in Nigerian pregnant women and concluded that 100g OGTT criteria was more stringent than that of 75g OGTT in identifying GDM. Sreedevi E et al [8] presented a paper that deals with study and development of algorithm to develop an initial stage expert system to provide diagnosis to the pregnant women who are suffering from GDM by means of OGTT.…”

Section: Literature Surveymentioning

confidence: 99%

Application of computational technique in design of classifier for early detection of gestational diabetes mellitus

Muller¹,

Sundaram²,

Nirmala³

et al. 2015

ams

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Gestational Diabetes Mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. 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 India, early detection and prevention will be more cost effective. Oral Glucose Tolerance Test (OGTT) is the crucial method for diagnosing GDM done usually between 24th and 28th week of pregnancy. The proposed work focuses on early detection of GDM without a visit to the hospital for women who are pregnant for the second time onwards (multigravida patients). A decision support system using Multilayer Neural Network which learns to classify GDM and non GDM patients using Back 3328 Priya Shirley Muller et al. Propagation learning algorithm is developed. The classifier proves to be an efficient model for diagnosis of GDM without the conventional method of blood test by providing newly designed parameters as inputs to the network.

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Section: Literature Surveymentioning

confidence: 99%

Application of computational technique in design of classifier for early detection of gestational diabetes mellitus

Muller¹,

Sundaram²,

Nirmala³

et al. 2015

ams

View full text Add to dashboard Cite

show abstract

Diagnosis of Gestational Diabetes Mellitus using Radial Basis function

Muller

Nirmala

2016

2016 Online International Conference on Green Engineering and Technologies (IC-GET)

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Rigorous assessment of data mining algorithms in gestational diabetes mellitus prediction

Reddy

Sethi

Rajender³

2022

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Gestational diabetes mellitus (GDM) is the type of diabetes that affects pregnant women due to high blood sugar levels. The women with gestational diabetes have a chance of miscarriage during pregnancy and having chance of developing type-2 diabetes in the future. It is a general practice to take proper diabetes test like OGTT to detect GDM. This test is to be done during 24 to 28 weeks of pregnancy. In addition, the use of machine learning could be exploited for predicting gestational diabetes. The main goal of this work is to propose optimal ML algorithms for effective prediction of gestational diabetes mellitus and there by avoid it’s side effects and future complications. In this work different machine algorithms are planned to be compared for their performance in predicting GDM. Before analysing the algorithms they are implemented using 10 fold cross validation technique to obtain better performance. The algorithms implemented are Linear Discriminant Analysis, Mixture Discriminant Analysis, Quadratic Discriminant Analysis, Flexible Discriminant Analysis, Regularized Discriminant Analysis and Feed Forward Neural Networks. These algorithms are compared depending on performance measures accuracy, kappa statistic, sensitivity, specificity, precision and F-measure. Then feed forward neural networks and Flexible Discriminant Analysis are obtained as optimal in this work.

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Modelling effective diagnosis of risk complications in gestational diabetes mellitus: an e-diabetic expert system for pregnant women

Cited by 5 publications

References 0 publications

Application of computational technique in design of classifier for early detection of gestational diabetes mellitus

Application of computational technique in design of classifier for early detection of gestational diabetes mellitus

Diagnosis of Gestational Diabetes Mellitus using Radial Basis function

Rigorous assessment of data mining algorithms in gestational diabetes mellitus prediction

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