School of Science, RMIT University, Melbourne, Victoria, Australia.
School of Science, Al-Baha University, Al-Baha, Saudi Arabia.
PLoS One. 2023 Mar 1;18(3):e0282426. doi: 10.1371/journal.pone.0282426. eCollection 2023.
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.
儿童 1 型糖尿病（T1D）发病率的上升是一个日益严重的全球性问题。已知遗传和环境因素促成了儿童 T1D 的发生。使用关键绩效指标（KPI）来预测儿童 T1D 发展的最佳模型将有助于医疗从业者制定干预计划。本文首次构建了一个模型，以预测沙特阿拉伯 0-14 岁儿童发生 T1D 的风险，并确定其重要的 KPI。使用了机器学习方法，即逻辑回归、随机森林、支持向量机、朴素贝叶斯和人工神经网络，并比较了它们的相对性能。分析是在来自沙特阿拉伯三个地区的基于人群的病例对照研究中进行的。数据集（n=1142）包含人口统计学和社会经济状况、遗传和疾病史、营养史、产科史和产妇特征。病例组和对照组之间的比较表明，大多数儿童（病例=68%，对照组=88%）来自城市地区，69%（病例）和 66%（对照组）是足月分娩，31%的病例组是剖宫产分娩，高于对照组（χ2=4.12，P 值=0.042）。使用所有可用的环境和家族史因素构建了模型。使用曲线下面积、敏感性、F 分数和精度来评估模型的效能。全逻辑回归模型的表现优于其他模型，准确性=0.77，敏感性、F 分数和精度为 0.70，AUC=0.83。最重要的 KPI 是早期接触牛奶（OR=2.92，P=0.000）、出生体重>4kg（OR=3.11，P=0.007）、居住（农村）（OR=3.74，P=0.000）、家族史（一级和二级）和产妇年龄>25 岁。这里呈现的结果可以帮助医疗保健提供者收集和监测有影响力的 KPI，并制定干预策略，以降低沙特阿拉伯儿童 T1D 的发病率。
