Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait--a cohort study.
Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait--a cohort study.
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DOI：
10.1136/bmjopen-2012-002457
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发表时间：
2013-05-14
期刊：
影响因子：
2.9
通讯作者：
Thanaraj TA
中科院分区：
文献类型：
--
作者：
Farran B;Channanath AM;Behbehani K;Thanaraj TA
我们使用机器学习算法对来自科威特的数据建立了糖尿病、高血压和合并症的分类模型和风险评估工具。我们模拟了糖尿病患者发展为高血压的倾向性增加，反之亦然。我们确定了种族（和本地人与外籍移民）和使用区域数据进行风险评估的重要性。回顾性队列研究。使用了四种机器学习技术：逻辑回归、k-最近邻（k-NN）、多因素降维和支持向量机。该研究使用五重交叉验证，以获得概括的准确性和错误。科威特卫生网络（KHN），整合了来自科威特初级卫生中心和医院的数据。270 172名医院就诊者（其中89 858人患有糖尿病，58 745人患有高血压，30 522人患有合并症），包括科威特本地人、亚洲人和阿拉伯侨民。 2型糖尿病、高血压和合并症。仅使用简单的非实验室参数即可实现>85%（糖尿病）和>90%（高血压）的分类准确度。基于k-NN分类模型的风险评估工具能够将75%的糖尿病患者和94%的高血压患者分配为“高”风险。只有5%的糖尿病患者被认为是“低”风险。针对亚洲的模型和评估表现更好。对一般人群或高血压人群中糖尿病的病理状况和高血压的病理状况进行建模。结合糖尿病（或高血压）的两个组件模型构建的两阶段总体分类模型和风险评估工具，比单个模型表现得更好。第一次提供了科威特这一国际大都市国家的糖尿病、高血压和合并症数据。这使我们能够应用四种不同的病例对照模型来评估风险。这些工具有助于对人口进行初步非侵入性评估。种族被认为是显着的预测模型。风险评估需要使用区域数据，因为我们证明了美国糖尿病协会在线计算器对科威特数据的适用性。
We build classification models and risk assessment tools for diabetes, hypertension and comorbidity using machine-learning algorithms on data from Kuwait. We model the increased proneness in diabetic patients to develop hypertension and vice versa. We ascertain the importance of ethnicity (and natives vs expatriate migrants) and of using regional data in risk assessment. Retrospective cohort study. Four machine-learning techniques were used: logistic regression, k-nearest neighbours (k-NN), multifactor dimensionality reduction and support vector machines. The study uses fivefold cross validation to obtain generalisation accuracies and errors. Kuwait Health Network (KHN) that integrates data from primary health centres and hospitals in Kuwait. 270 172 hospital visitors (of which, 89 858 are diabetic, 58 745 hypertensive and 30 522 comorbid) comprising Kuwaiti natives, Asian and Arab expatriates. Incident type 2 diabetes, hypertension and comorbidity. Classification accuracies of >85% (for diabetes) and >90% (for hypertension) are achieved using only simple non-laboratory-based parameters. Risk assessment tools based on k-NN classification models are able to assign ‘high’ risk to 75% of diabetic patients and to 94% of hypertensive patients. Only 5% of diabetic patients are seen assigned ‘low’ risk. Asian-specific models and assessments perform even better. Pathological conditions of diabetes in the general population or in hypertensive population and those of hypertension are modelled. Two-stage aggregate classification models and risk assessment tools, built combining both the component models on diabetes (or on hypertension), perform better than individual models. Data on diabetes, hypertension and comorbidity from the cosmopolitan State of Kuwait are available for the first time. This enabled us to apply four different case–control models to assess risks. These tools aid in the preliminary non-intrusive assessment of the population. Ethnicity is seen significant to the predictive models. Risk assessments need to be developed using regional data as we demonstrate the applicability of the American Diabetes Association online calculator on data from Kuwait.
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