Abdalrada Ahmad Shaker, Abawajy Jemal, Al-Quraishi Tahsien, Islam Sheikh Mohammed Shariful
Faculty of Computer Science and Information Technology, Wasit University, Al Kut, Iraq.
School of Information Technology, Deakin University, Melbourne, Victoria Australia.
J Diabetes Metab Disord. 2022 Jan 12;21(1):251-261. doi: 10.1007/s40200-021-00968-z. eCollection 2022 Jun.
Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity. In this paper, we aimed to develop and evaluate a two-stage machine learning (ML) model to predict the co-occurrence of DM and CVD.
We used the diabetes complications screening research initiative (DiScRi) dataset containing >200 variables from >2000 participants. In the first stage, we used two ML models (logistic regression and Evimp functions) implemented in multivariate adaptive regression splines model to infer the significant common risk factors for DM and CVD and applied the correlation matrix to reduce redundancy. In the second stage, we used classification and regression algorithm to develop our model. We evaluated the prediction models using prediction accuracy, sensitivity and specificity as performance metrics.
Common risk factors for DM and CVD co-occurrence was family history of the diseases, gender, deep breathing heart rate change, lying to standing blood pressure change, HbA1c, HDL and TC\HDL ratio. The predictive model showed that the participants with HbA1c >6.45 and TC\HDL ratio > 5.5 were at risk of developing both diseases (97.9% probability). In contrast, participants with HbA1c >6.45 and TC\HDL ratio ≤ 5.5 were more likely to have only DM (84.5% probability) and those with HbA1c ≤5.45 and HDL >1.45 were likely to be healthy (82.4%. probability). Further, participants with HbA1c ≤5.45 and HDL <1.45 were at risk of only CVD (100% probability). The predictive accuracy of the ML model to detect co-occurrence of DM and CVD is 94.09%, sensitivity 93.5%, and specificity 95.8%.
Our ML model can significantly predict with high accuracy the co-occurrence of DM and CVD in people attending a screening program. This might help in early detection of patients with DM and CVD who could benefit from preventive treatment and reduce future healthcare burden.
糖尿病（DM）和心血管疾病（CVD）在全球造成了巨大的医疗负担，且常常并存。当前的方法往往无法识别许多同时患有糖尿病和心血管疾病的人，导致就医延迟、并发症增加和发病率上升。在本文中，我们旨在开发并评估一种两阶段机器学习（ML）模型，以预测糖尿病和心血管疾病的并存情况。
我们使用了糖尿病并发症筛查研究倡议（DiScRi）数据集，该数据集包含来自2000多名参与者的200多个变量。在第一阶段，我们使用多元自适应回归样条模型中实现的两个ML模型（逻辑回归和Evimp函数）来推断糖尿病和心血管疾病的显著共同风险因素，并应用相关矩阵来减少冗余。在第二阶段，我们使用分类和回归算法来开发我们的模型。我们使用预测准确性、敏感性和特异性作为性能指标来评估预测模型。
糖尿病和心血管疾病并存的共同风险因素是疾病家族史、性别、深呼吸心率变化、卧位到立位血压变化、糖化血红蛋白（HbA1c）、高密度脂蛋白（HDL）和总胆固醇/高密度脂蛋白比值（TC\HDL）。预测模型显示，糖化血红蛋白>6.45且总胆固醇/高密度脂蛋白比值>5.5的参与者有患这两种疾病的风险（概率为97.9%）。相比之下，糖化血红蛋白>6.45且总胆固醇/高密度脂蛋白比值≤5.5的参与者更有可能仅患糖尿病（概率为84.5%）；而糖化血红蛋白≤5.45且高密度脂蛋白>1.45的参与者可能健康（概率为82.4%）。此外，糖化血红蛋白≤5.45且高密度脂蛋白<1.45的参与者有仅患心血管疾病的风险（概率为100%）。该ML模型检测糖尿病和心血管疾病并存情况的预测准确性为94.09%，敏感性为93.5%，特异性为95.8%。
我们的ML模型能够以高精度显著预测参加筛查项目的人群中糖尿病和心血管疾病的并存情况。这可能有助于早期发现那些能从预防性治疗中获益的糖尿病和心血管疾病患者，并减轻未来的医疗负担。
