A high proportion of older adults with type 2 diabetes (T2D) often develop cardiovascular diseases (CVD). Diagnosis and regular monitoring of their multimorbidity is clinically and economically resource intensive. The interconnectedness of their health data and disease progression pathways can potentially reveal the multimorbidity risk if carefully analysed by data mining and network analysis techniques. This study proposed a risk prediction model utilising administrative data that uses network-based features and machine learning techniques to assess the risk of CVD in T2D patients. For this, two cohorts (i.e., patients with both T2D and CVD and patients with only T2D) were identified from an administrative dataset collected from the private healthcare funds based in Australia. Two baseline disease networks were generated from two study cohorts. A final disease network was then generated from two baseline disease networks through normalisation. This study extracted some social network-based features (i.e., the prevalence of comorbidities, transition patterns and clustering membership) from the final disease network and some demographic characteristics directly from the dataset. These risk factors were then used to develop six machine learning prediction models to assess the risk of CVD in patients with T2D. The classifiers accuracy ranged from 79% to 88% shows the potential of the network- and machine learning-based risk prediction model utilising administrative data. The proposed risk prediction model could be useful for medical practice as well as stakeholders to develop health management programs for patients at a high risk of developing chronic diseases.

高比例的2型糖尿病（T2D）老年人通常会发展为心血管疾病（CVD）。临床上和经济上都需要对它们的多发病进行诊断和定期监测。如果通过数据挖掘和网络分析技术进行仔细分析，他们的健康数据和疾病进展路径之间的相互联系可能会揭示多发病风险。这项研究提出了利用管理数据的风险预测模型，该管理数据使用基于网络的功能和机器学习技术来评估T2D患者的CVD风险。为此，从澳大利亚私人医疗基金收集的行政数据集中确定了两个队列（即患有T2D和CVD的患者以及仅有T2D的患者）。来自两个研究队列的两个基线疾病网络。然后通过归一化从两个基准疾病网络中生成最终的疾病网络。这项研究从最终疾病网络中提取了一些基于社交网络的特征（即合并症的流行，过渡模式和聚类成员），并从数据集中直接提取了一些人口统计学特征。然后将这些风险因素用于开发六个机器学习预测模型，以评估T2D患者中CVD的风险。分类器的准确性介于79％到88％之间，显示了利用管理数据的基于网络和机器学习的风险预测模型的潜力。拟议的风险预测模型可能对医学实践以及利益相关者有用，以便为患有慢性疾病高风险的患者制定健康管理计划。