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Skip search results from other journals and go to results- 5 JMIRx Med
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Gestational diabetes mellitus (GDM) is a type of glucose intolerance or hyperglycemia indicated by the onset of elevated blood glucose levels (BGLs) during pregnancy [1]. It is one of the most common pregnancy-related complications, with an increasing prevalence both worldwide and in Australia [2,3]. According to the Australian Institute of Health and Welfare, GDM was diagnosed in 1 out of 6 women who gave birth during 2020‐2021 [4].
JMIR Diabetes 2025;10:e65328
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This is a critical investigation, as reducing fasting hyperglycemia is key to preventing the progression of type 2 diabetes in those with i-IFG [6,26]. To inform the design and implementation of this RCT, we propose conducting a proof-of-concept study among individuals with i-IFG, with the following objectives.
JMIR Res Protoc 2025;14:e59842
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This is the peer-review report for “Machine Learning–Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals.”
This paper [1] introduces a machine learning (ML) methodology for predicting hyperglycemia in one of the cohorts taken from a suburban Nigerian region. The authors present the details of the methodology for participant recruitment and screening, data analysis, and selection of ML models.
The introduction and motivation behind the work are well written.
JMIRx Med 2024;5:e60389
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This is the peer-review report for “Machine Learning–Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals.”
In this paper [1], describe dataset features in more detail and its total size and size (train/test) as a table.
Pseudocode/flowchart and algorithm steps need to be inserted.
Time spent needs to be measured in the experimental results.
Limitation and Discussion sections need to be inserted.
JMIRx Med 2024;5:e60393
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This is the peer-review report for “Machine Learning–Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals.”
Overall strong paper [1]! This was an interesting study on the use of machine learning to predict hyperglycemia in a cohort of undiagnosed individuals from Nigeria. I feel like this work is a strong contribution to the field of public health, especially within the context of noncommunicable diseases in developing countries.
JMIRx Med 2024;5:e60853
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This is the authors’ response to peer-review reports for “Machine Learning–Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals.”
1. In this paper [2], describe dataset features in more detail and its total size and size (train/test) as a table.
Response: The comprehensive list of the dataset features and size are described in Additional File 2, which has now been added to the revised submission.
JMIRx Med 2024;5:e60174
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In the context of hyperglycemia prediction, precision would be the count of accurate predictions of hyperglycemia divided by the total instances where the classifier predicted “hyperglycemia,” regardless of correctness. Recall, on the other hand, measures the effectiveness of a classifier in correctly identifying members of a class by dividing the number of correctly identified instances by the total number of actual members in that class.
JMIRx Med 2024;5:e56993
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Cardiovascular Comorbidities in COVID-19: Comprehensive Analysis of Key Topics
hyperglycemia
Interact J Med Res 2024;13:e55699
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Analysis of Self-Care Activities in Type 2 Diabetes in Brazil: Protocol for a Scoping Review
T2 D occurs when hyperglycemia is the result of insulin resistance that has been established gradually over many years until diagnosis [1,2]. In this case, the person does not present the classic signs of diabetes, such as dehydration or involuntary weight loss, and the diagnosis comes with acute or chronic complications of diabetes [2].
Diabetes complications are categorized as microvascular and macrovascular disorders.
JMIR Res Protoc 2024;13:e49105
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