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Type 2 diabetes mellitus develops gradually and is often asymptomatic in early stages.Clinical ManifestationsWhen symptoms appear, they include fatigue, blurred vision, pruritus, delayed wound healing, and recurrent infections, particularly candidal infections. Peripheral neuropathy may present as numbness or tingling in the extremities. Classic hyperglycemia symptoms—polyuria, polydipsia, and polyphagia—are less common. Most patients are overweight and frequently have associated...
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DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...
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Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance, in which target tissues such as the liver, muscle, and adipose tissue respond poorly to insulin. It is also associated with inadequate compensatory insulin secretion, where pancreatic β-cells fail to produce sufficient insulin. Together, these abnormalities lead to persistent hyperglycemia.EtiologyT2DM develops through a complex interaction of genetic predisposition and environmental or...
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Screening for pre-diabetes using support vector machine model.

Jai Won Chung, Won Jae Kim, Soo Beom Choi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary

    A new intelligence-based screening model using support vector machine (SVM) shows improved accuracy in identifying pre-diabetes. This advanced model aids in early detection, potentially reducing the global prevalence of diabetes through timely interventions.

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    Area of Science:

    • Endocrinology
    • Public Health
    • Machine Learning in Healthcare

    Background:

    • Global diabetes prevalence is rising, necessitating early detection of pre-diabetes.
    • Pre-diabetes can lead to serious complications and the development of type 2 diabetes.
    • Effective screening tools are crucial for timely intervention and diabetes prevention.

    Purpose of the Study:

    • To develop and validate an intelligence-based screening model for pre-diabetes.
    • To enhance early identification of individuals at risk for diabetes.
    • To compare the performance of the novel model against existing screening methods.

    Main Methods:

    • Utilized Korean National Health and Nutrition Examination Survey (KNHANES) data (2010-2011).
    • Developed a pre-diabetes screening model using Support Vector Machine (SVM).
    • Validated the SVM model internally and externally, comparing it with a logistic regression-based screening score model.

    Main Results:

    • The SVM model demonstrated superior performance over the logistic regression model.
    • Internal validation showed an Area Under the Curve (AUC) of 0.761 for the SVM model.
    • External validation yielded an AUC of 0.731 for the SVM model, outperforming the comparison model (0.712).

    Conclusions:

    • The developed SVM model is a more effective tool for pre-diabetes screening.
    • Early identification of pre-diabetes via this model can support diabetes prevention strategies.
    • This intelligence-based approach offers a promising method for public health initiatives targeting diabetes reduction.