2022
DOI: 10.1016/j.cmpb.2022.107180
|View full text |Cite
|
Sign up to set email alerts
|

Harnessing machine learning models for non-invasive pre-diabetes screening in children and adolescents

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
7
0

Year Published

2023
2023
2025
2025

Publication Types

Select...
9
1

Relationship

0
10

Authors

Journals

citations
Cited by 18 publications
(7 citation statements)
references
References 40 publications
0
7
0
Order By: Relevance
“…The ML model based on extreme gradient boosting (XGB) was selected in our study because of its generalizability, low risk of overfitting, high interpretability [25], and high scalability [34]. XGB has been confirmed to be a reliable method for recognizing patterns in other diseases such as lupus erythematosus [16], traumatic brain injury-induced coagulopathy [35], epilepsy [36], diabetes [37], Alzheimer's disease [38,39], HIV [40,41], or different types of cancer [42][43][44][45][46]. We, therefore, used the aforementioned ML technique to determine which factors were most predictive of disease severity in a closed group of patients hospitalized for COVID-19 during the first two months of the pandemic, a time when the population did not yet have herd immunity and had not yet been vaccinated.…”
Section: Discussionmentioning
confidence: 99%
“…The ML model based on extreme gradient boosting (XGB) was selected in our study because of its generalizability, low risk of overfitting, high interpretability [25], and high scalability [34]. XGB has been confirmed to be a reliable method for recognizing patterns in other diseases such as lupus erythematosus [16], traumatic brain injury-induced coagulopathy [35], epilepsy [36], diabetes [37], Alzheimer's disease [38,39], HIV [40,41], or different types of cancer [42][43][44][45][46]. We, therefore, used the aforementioned ML technique to determine which factors were most predictive of disease severity in a closed group of patients hospitalized for COVID-19 during the first two months of the pandemic, a time when the population did not yet have herd immunity and had not yet been vaccinated.…”
Section: Discussionmentioning
confidence: 99%
“…Savitesh predicted the risk of pre-diabetes in children and adolescents and found that XG Boost was the best classification model with a 10-fold cross-validation score of up to 90.13%. Savitesh integrated the XG Boost algorithm into a screening tool for completing the automatic prediction of pre-diabetes [ 37 ]. Shoukun performed miner fatigue identification based on physiological indicators from ECG and EMG and found that the XG Boost model had the best accuracy and robustness with a recognition accuracy of 89.47% and AUC of 0.90, the recognition of miner fatigue based on the XG Boost model is feasible [ 38 ].…”
Section: Discussionmentioning
confidence: 99%
“…There are different works with ML for diabetes detection that used the Pima Indian dataset, which is a dataset composed of 9 characteristics and 768 patients [14][15][16][17]. For example, in [18], they implemented a model with ML using cross-validation to predict diabetes with noninvasive instruments, and they applied six ML models with different assessment metrics. As a result, the XG boost classifier obtained the best metric with 0.9013 in cross-validation, and the model with the lowest performance was SVM with 61.17%.…”
Section: Related Workmentioning
confidence: 99%