Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients’ health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the patterns of various observations, the research contributes to the work of practitioners by providing a multifaceted understanding of individual and public health.

中文翻译：

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挖掘健康知识图以预测健康风险

如今，分类模型已在医疗保健中广泛采用，旨在支持从业人员进行疾病诊断和减少人为错误。挑战是利用有效的方法来挖掘医学领域的现实世界数据，因为已经提出了许多不同的模型，但结果各不相同。大量研究人员关注分类模型中实时数据集的多样性问题。一些先前的工作开发了包括均质图的方法，用于知识表示和知识发现。但是，这种方法在发现元素之间的不同关系方面很弱。在本文中，我们提出了一种创新的分类模型，用于从患者的个人健康信息库中发现知识。该模型从国家健康和营养检查调查（NHANES）中的大量数据中发现医学领域知识。知识在异构知识图中被概念化。在该模型的基础上，开发了一种创新方法，以帮助发现人们可能遭受的潜在疾病，并对患者的健康风险进行分类。通过与在经验实验中也基于NHANES数据集建立的基线模型进行比较，评估了提出的模型。提出的模型的性能是有希望的。本文使用专为基于域的数据设计的创新分类模型，为数据挖掘领域的知识发展做出了重要贡献。此外，通过访问各种观察结果的模式，