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Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis

机译：将数据挖掘与基于案例的推理相结合来进行慢性疾病的预后和诊断

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摘要

The threats to people's health from chronic diseases are always exist and increasing gradually. How to decrease these threats is an important issue in medical treatment. Thus, this paper suggests a model of a chronic diseases prognosis and diagnosis system integrating data mining (DM) and case-based reasoning (CBR). The main processes of the system include: (1) adopting data mining techniques to discover the implicit meaningful rules from health examination data, (2) using the extracted rules for the specific chronic diseases prognosis, (3) employing CBR to support the chronic diseases diagnosis and treatments, and (4) expanding these processes to work within a system for the convenience of chronic diseases knowledge creating, organizing, refining, and sharing. The experiment data are collected from a professional health examination center, MJ health screening center, and implemented through the system for analysis. The findings are considered as helpful references for doctors and patients in chronic diseases treatments.
机译：慢性病对人们健康的威胁一直存在，并且正在逐步增加。如何减少这些威胁是医学治疗中的重要问题。因此，本文提出了一种结合数据挖掘（DM）和基于案例的推理（CBR）的慢性疾病预后和诊断系统模型。该系统的主要过程包括：（1）采用数据挖掘技术从健康检查数据中发现隐含的有意义的规则；（2）使用提取的特定慢性病预后规则；（3）使用CBR支持慢性病诊断和治疗，以及（4）将这些过程扩展到系统中以方便慢性病知识的创建，组织，完善和共享。实验数据是从专业健康检查中心，MJ健康检查中心收集的，并通过系统进行分析。该发现被认为是对医生和慢性病治疗患者有用的参考。

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