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Predicting the development of T1D and identifying its Key Performance Indicators in children; a case-control study in
Ahood Alazwari1,2, Alice Johnstone1, Laleh Tafakori1
1School of Science, RMIT University, Melbourne, Victoria, Australia.
Insights
This study developed a predictive model for childhood type 1 diabetes (T1D) in Saudi Arabia. Key risk factors identified include early cow's milk exposure and family history, aiding early intervention strategies for T1D prevention.
Area of Science:
- Pediatric Endocrinology
- Computational Epidemiology
- Public Health Research
Background:
- Childhood type 1 diabetes (T1D) incidence is rising globally.
- Predictive models for T1D risk in children are crucial for intervention.
- Genetic and environmental factors are known contributors to T1D development.
Purpose of the Study:
- To develop and validate a predictive model for childhood T1D risk in Saudi Arabia.
- To identify significant Key Performance Indicators (KPIs) for T1D development in children (0-14 years).
- To compare the performance of various machine learning algorithms for T1D risk prediction.
Main Methods:
- A population-based case-control study involving 1,142 participants from three Saudi Arabian regions.
- Utilized machine learning algorithms: Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Artificial Neural Network.
- Evaluated model performance using Area Under the Curve (AUC), Sensitivity, F Score, and Precision.
Main Results:
- Logistic Regression demonstrated superior performance with an Accuracy of 0.77, Sensitivity, F Score, and Precision of 0.70, and AUC of 0.83.
- Significant KPIs for T1D risk included early cow's milk exposure (OR=2.92), birth weight >4 Kg (OR=3.11), rural residency (OR=3.74), family history, and maternal age >25 years.
- Caesarean delivery was significantly higher in the T1D case group (31%) compared to controls (P=0.042).
Conclusions:
- The developed logistic regression model effectively predicts childhood T1D risk in Saudi Arabia.
- Identified key environmental and familial factors can guide healthcare providers in monitoring and intervention.
- Results support targeted strategies to mitigate the increasing incidence of childhood T1D.
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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