Early-stage disease risk prediction can be beneficial to improve the health of the mass and can reduce the economic burden of late treatment. Machine learning has played a pivotal role in predictive systems, which requires achieving a specific degree of accuracy for healthcare systems. Most recently researchers have found the necessity of bridging between epidemiology and machine learning classifications toward health risk prediction. This work proposes an epidemiology knowledge-driven unique model that follows the principle of association rule-based ontology to select features and classification techniques. The goal of this approach is to generalize a framework for future robust systems to predict the likelihood of diseases, which can be executed in the edge computing environment. The framework introduces epidemiological library and structured attribute set along with the library of precaution to derive the disease risk-prediction process. To investigate the adoption of the epidemiology knowledge-driven model, we considered a real dataset of early-stage likelihood prediction of diabetes and carried out a set of experiments for highlighting the significance of several epidemiological factors. The classification aspect of the framework is further compared with widely accepted approaches for machine learning based healthcare, which shows the novelty of the proposed model.

早期疾病风险预测可以有益于改善群众的健康状况，并可以减轻后期治疗的经济负担。机器学习在预测系统中扮演着举足轻重的角色，这要求医疗保健系统达到特定的准确度。最近，研究人员发现有必要在流行病学和机器学习分类之间架起桥梁，以预测健康风险。这项工作提出了一种流行病学知识驱动的独特模型，该模型遵循基于关联规则的本体原理来选择特征和分类技术。此方法的目标是为将来的健壮系统提供一个框架，以预测可以在边缘计算环境中执行的疾病的可能性。该框架引入了流行病学库和结构化属性集以及预防措施库，以推导疾病风险预测过程。为了研究流行病学知识驱动模型的采用，我们考虑了糖尿病早期可能性预测的真实数据集，并进行了一系列实验以突出几种流行病学因素的重要性。该框架的分类方面进一步与基于机器学习的医疗保健被广泛接受的方法进行了比较，这表明了所提出模型的新颖性。我们考虑了糖尿病早期可能性预测的真实数据集，并进行了一系列实验以突出几种流行病学因素的重要性。该框架的分类方面进一步与基于机器学习的医疗保健被广泛接受的方法进行了比较，这表明了所提出模型的新颖性。我们考虑了糖尿病早期可能性预测的真实数据集，并进行了一系列实验以突出几种流行病学因素的重要性。该框架的分类方面进一步与基于机器学习的医疗保健被广泛接受的方法进行了比较，这表明了所提出模型的新颖性。