Machine learning to predict rapid progression of carotid atherosclerosis in patients with impaired glucose tolerance.
Machine learning to predict rapid progression of carotid atherosclerosis in patients with impaired glucose tolerance.
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DOI：
10.1186/s13637-016-0049-6
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
ACT NOW Study Investigators
中科院分区：
文献类型：
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作者：
Hu X;Reaven PD;Saremi A;Liu N;Abbasi MA;Liu H;Migrino RQ;ACT NOW Study Investigators
前驱糖尿病是一种主要的流行病，与不良的心脑血管结局相关。早期识别动脉粥样硬化快速进展的患者可能有利于改善风险分层。在本文中，我们研究了影响预测的重要因素，使用几种机器学习方法，葡萄糖耐量受损（IGT）参与者的颈动脉内膜中层厚度的快速进展。在Actos Now预防糖尿病（ACT NOW）研究中，382名IGT参与者在基线和15-18个月时接受了颈动脉内膜中层厚度（CIMT）超声评估，并被分为快速进展者（RP，n = 39，58 ± 17.5 μM变化）和非快速进展者（NRP，n = 343，5.8 ± 20 μM变化，p < 0.001相对于RP）。为了处理由人口统计学，临床和实验室变量组成的复杂多模态数据，我们提出了一个通用的数据驱动框架来研究ACT NOW数据集。特别是，我们首先采用基于Fisher Score的特征选择方法来识别最有效的变量，然后提出了一种基于概率贝叶斯的学习方法进行预测。使用受试者工作特征曲线下面积（AUC）分析和Brier评分进行方法和因素的比较。实验结果表明，所提出的学习方法在识别或预测RP方面表现良好。在这些方法中，与多层感知器（0.729，0.086）和随机森林（0.642，0.10）相比，朴素贝叶斯的性能最好（AUC 0.797，Brier得分0.085）。结果还表明，特征选择对数据预测性能有显着的积极影响。通过处理多模态数据，所提出的学习方法在预测糖尿病前期患者快速动脉粥样硬化进展的风险方面显示出有效性。所提出的框架在具有相对少量受试者的典型多维临床数据集中的结果预测中表现出实用性，将机器学习方法的潜在实用性扩展到超大规模数据集之外。
Prediabetes is a major epidemic and is associated with adverse cardio-cerebrovascular outcomes. Early identification of patients who will develop rapid progression of atherosclerosis could be beneficial for improved risk stratification. In this paper, we investigate important factors impacting the prediction, using several machine learning methods, of rapid progression of carotid intima-media thickness in impaired glucose tolerance (IGT) participants. In the Actos Now for Prevention of Diabetes (ACT NOW) study, 382 participants with IGT underwent carotid intima-media thickness (CIMT) ultrasound evaluation at baseline and at 15–18 months, and were divided into rapid progressors (RP, n = 39, 58 ± 17.5 μM change) and non-rapid progressors (NRP, n = 343, 5.8 ± 20 μM change, p < 0.001 versus RP). To deal with complex multi-modal data consisting of demographic, clinical, and laboratory variables, we propose a general data-driven framework to investigate the ACT NOW dataset. In particular, we first employed a Fisher Score-based feature selection method to identify the most effective variables and then proposed a probabilistic Bayes-based learning method for the prediction. Comparison of the methods and factors was conducted using area under the receiver operating characteristic curve (AUC) analyses and Brier score. The experimental results show that the proposed learning methods performed well in identifying or predicting RP. Among the methods, the performance of Naïve Bayes was the best (AUC 0.797, Brier score 0.085) compared to multilayer perceptron (0.729, 0.086) and random forest (0.642, 0.10). The results also show that feature selection has a significant positive impact on the data prediction performance. By dealing with multi-modal data, the proposed learning methods show effectiveness in predicting prediabetics at risk for rapid atherosclerosis progression. The proposed framework demonstrated utility in outcome prediction in a typical multidimensional clinical dataset with a relatively small number of subjects, extending the potential utility of machine learning approaches beyond extremely large-scale datasets.