Diagnosis of Gestational Diabetes Mellitus using Radial Basis function

Muller, P.; Nirmala, M.

doi:10.1109/get.2016.7916859

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…Classification accuracy (accuracy = (true positive for T2DM + true negative for non-T2DM)/(true positive for T2DM + true negative for non-T2DM + false positive for non-T2DM + false negative for T2DM); Nai-arun and Sittidech, [20]) was 82% for Least Square Support Vector Machine [18] and 85% for Adaptive Network Based Fuzzy Inference System [19]. Discriminant analysis has also been used to discriminate between non-GDM and GDM patients to identify the most important risk factors for developing GDM [21][22][23]. Although discriminant analyses have been used to identify risk factors related to the development of diabetes or its complications, to our knowledge, the discriminant analysis approach has not been suggested as an approach in assessing the effectiveness of health-and-wellness programs.…”

Section: Introductionmentioning

confidence: 99%

Fisher’s Linear Discriminant Function Analysis and its Potential Utility as a Tool for the Assessment of Health-and-Wellness Programs in Indigenous Communities

Zuk¹,

Martin²,

Tsuji³

2023

Preprint

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<p>Diabetes mellitus is a growing public health problem affecting persons in both developed and developing nations. The prevalence of type 2 diabetes mellitus (T2DM) is reported to be several times higher among Indigenous populations compared to their non-Indigenous counterparts. Discriminant function analysis (DFA) is a potential tool that can be used to quantitatively evaluate the effectiveness of Indigenous health-and-wellness programs (e.g., on-the-land programs, T2DM interventions), by creating a type of pre-and-post-program scoring system. As the communities of the Eeyou Istchee territory, subarctic Quebec, Canada, have varying degrees of isolation, we derived a DFA tool for point-of-contact evaluations to aid in monitoring and assessment of health-and-wellness programs in rural and remote locations. We developed several DFA models to discriminate between those with and without T2DM status using age, fasting blood glucose, body mass index, waist girth, systolic and diastolic blood pressure, high-density lipoprotein, triglycerides, and total cholesterol in participants from the Eeyou Istchee. The models showed a ~97% specificity (i.e., true positives for non-T2DM) in classification. This study highlights how varying risk factor models can be used to discriminate those without T2DM with high specificity among James Bay Cree communities in Canada.</p>

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Section: Introductionmentioning

confidence: 99%

Fisher’s Linear Discriminant Function Analysis and its Potential Utility as a Tool for the Assessment of Health-and-Wellness Programs in Indigenous Communities

Zuk¹,

Martin²,

Tsuji³

2023

Preprint

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“…Gestational diabetes mellitus (GDM) (1)(2)(3) is a syndrome that occurs among women during their pregnancy. World Health Organization (WHO) stated that the prevalence of GDM is increasing every year owing to lifestyle changes and the high number of Type 2 diabetes (T2D) patients.…”

Section: Introductionmentioning

confidence: 99%

Prognosis Model for Gestational Diabetes Using Machine Learning Techniques

Amarnath¹,

Selvamani²,

Varadarajan³

2021

Sensors and Materials

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Gestational diabetes mellitus (GDM) is a syndrome that occurs among women during pregnancy and is characterized by lack of insulin hormone secretion. GDM occurs in about 4% of all pregnancies and is diagnosed at later stages of pregnancy. It can occur in women with no known history of diabetes. Since no signs or symptoms occur at the onset of GDM, it is possible to diagnose it only through screening tests. GDM poses some major health risks such as hormonal imbalance, delivery risks, and the development of Type 2 diabetes (T2D) after delivery. The condition can be diagnosed from the blood sugar level. Those diagnosed with GDM are likely to be obese, have a weak constitution, and be undergoing a stressful life or living in a stressful environment, eating unhealthy food, and living an unhealthy lifestyle. Other risk factors to be considered are family history, heredity, and the occurrence of diabetes in the past. Apart from diagnosis, the most crucial stage in managing GDM is its prognosis. If the disease is diagnosed at earlier stages, one can avoid its complications. Advanced technologies such as IoT and wearable sensors can help healthcare professionals in identifying the early signs and symptoms of GDM. In this scenario, data mining techniques are recommended for the prognosis of GDM using existing medical reports and risk factors related to women. A patient's medical history and their family history should be correlated with each other to find the likelihood of GDM occurrence. Classification is a technique in which a training dataset is used to predict the importance of related factors using an inference function. Our aim is to develop a prognosis model for GDM using a classification technique. A GDM prognosis model is developed using a training set of disease parameters along with an individual's risk factors. From the results of our experiments, it is inferred that the proposed model can be used for predicting the likelihood of GDM in its earlier stages.

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