School of Economics and Management, Dalian University of Technology, Dalian, China.
School of Management, Zhejiang University, Hangzhou, China.
PLoS One. 2020 Jul 9;15(7):e0235007. doi: 10.1371/journal.pone.0235007. eCollection 2020.
In this paper, we investigate the interaction impacts of body mass index (BMI) on the other important risk factors for venous thromboembolism (VTE), using deep venous thrombosis (DVT) patient data from the International Warfarin Pharmacogenetics Consortium (IWPC). We apply eight machine learning techniques, including naive Bayes classifier (NB), support vector machine (SVM), elastic net regression (ENET), logistic regression (LR), lasso regression (LAR), multivariate adaptive regression splines (MARS), boosted regression tree (BRT) and random forest model (RF). The RF method is selected as the best model for classification. Out of 33 features considered in this study, we identify 12 variables as relatively important risk factors for VTE. Finally, we examine the interaction impacts of BMI on these important VTE risk factors. We conclude that the impacts of risk factors on VTE incidence are varying across different BMI groups, and the variations are different for different risk factors. Therefore the interaction impacts of BMI on the other risk factors have to be taken into account in order to better understand the incidence of VTE.
在本文中，我们利用国际华法林药物基因组学合作研究（IWPC）的深静脉血栓（DVT）患者数据，研究了体重指数（BMI）对静脉血栓栓塞症（VTE）其他重要危险因素的交互影响。我们应用了八种机器学习技术，包括朴素贝叶斯分类器（NB）、支持向量机（SVM）、弹性网回归（ENET）、逻辑回归（LR）、套索回归（LAR）、多元自适应回归样条（MARS）、增强回归树（BRT）和随机森林模型（RF）。RF 方法被选为分类的最佳模型。在本研究中考虑的 33 个特征中，我们确定了 12 个变量作为 VTE 的相对重要危险因素。最后，我们检查了 BMI 对这些重要 VTE 危险因素的交互影响。我们得出结论，风险因素对 VTE 发生率的影响在不同 BMI 组之间是不同的，并且对于不同的风险因素，变化也不同。因此，为了更好地理解 VTE 的发生率，必须考虑 BMI 对其他风险因素的交互影响。
