ArticlePloS one2023
Predicting the development of T1D and identifying its Key Performance Indicators in children; a case-control study in Saudi Arabia.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Harnessing Machine Learning, a Subset of Artificial Intelligence, for Early Detection and Diagnosis of Type 1 Diabetes: A Systematic Review.International journal of molecular sciences · 2025Pooled it
- Review
- A deep learning framework with hybrid stacked sparse autoencoder for type 2 diabetes prediction.Scientific reports · 2025Article
- Gestational diabetes and parental type 2 diabetes as risk indicators for type 1 diabetes in offspring: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Article
- Article
- Ensemble neural network modelling for stratified HbA1c prediction: Integrating past glucose measurement as a predictor of glycaemic control.Bioinformation · 2025Article
- Article
- Predicting maternal risk level using machine learning models.BMC pregnancy and childbirth · 2024Article
- Factors associated with severe diabetic ketoacidosis in patients diagnosed with type 1 diabetes: a decade-long cross-sectional analysis.The Journal of international medical research · 2024Article
- Deep learning captures the effect of epistasis in multifactorial diseases.Frontiers in medicine · 2024Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing incidence of type 1 diabetes (T1D) in children is a growing global concern. It is known that genetic and environmental factors contribute to childhood T1D. An optimal model to predict the development of T1D in children using Key Performance Indicators (KPIs) would aid medical practitioners in developing intervention plans. This paper for the first time has built a model to predict the risk of developing T1D and identify its significant KPIs in children aged (0-14) in Saudi Arabia. Machine learning methods, namely Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Artificial Neural Network have been utilised and compared for their relative performance. Analyses were performed in a population-based case-control study from three Saudi Arabian regions. The dataset (n = 1,142) contained demographic and socioeconomic status, genetic and disease history, nutrition history, obstetric history, and maternal characteristics. The comparison between case and control groups showed that most children (cases = 68% and controls = 88%) are from urban areas, 69% (cases) and 66% (control) were delivered after a full-term pregnancy and 31% of cases group were delivered by caesarean, which was higher than the controls (χ2 = 4.12, P-value = 0.042). Models were built using all available environmental and family history factors. The efficacy of models was evaluated using Area Under the Curve, Sensitivity, F Score and Precision. Full logistic regression outperformed other models with Accuracy = 0.77, Sensitivity, F Score and Precision of 0.70, and AUC = 0.83. The most significant KPIs were early exposure to cow's milk (OR = 2.92, P = 0.000), birth weight >4 Kg (OR = 3.11, P = 0.007), residency(rural) (OR = 3.74, P = 0.000), family history (first and second degree), and maternal age >25 years. The results presented here can assist healthcare providers in collecting and monitoring influential KPIs and developing intervention strategies to reduce the childhood T1D incidence rate in Saudi Arabia.
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