基于模糊逻辑的患者定制医疗疾病推理方案

2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2015-11-30 DOI:10.1109/PACRIM.2015.7334872

Byungkwan Lee, E. Jeong, Jeongah Kim

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引用次数: 3

摘要

提出了一种基于模糊逻辑的患者定制医疗疾病推理方案。它由基于模糊的疾病规则模块(FDRM)和基于模糊的疾病推理模型(FDIM)组成。与传统的C4.5算法不同，基于模糊的疾病规则模块(FDRM)通过计算属性之间的条件支持度，利用条件支持度高的属性生成考虑属性之间关系的模糊规则。因此，由于所生成的模糊规则比传统的C4.5算法减少了更多的属性数量，使得规则的准确性得到了更大的提高。基于模糊的疾病推理模块(FDIM)不仅可以利用生成的模糊规则和患者的疾病信息对患者的疾病进行准确的推理，而且可以提前预防患者的疾病。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Disease Inference Scheme based on Fuzzy Logic for Patient's-customized Healthcare

This paper proposes a Disease Inference Scheme based on Fuzzy Logic for Patient's-customized Healthcare. It consists of the Fuzzy-based Disease Rules Module (FDRM) and the Fuzzy-based Disease Inference Model (FDIM). The Fuzzy-based Disease Rules Module (FDRM) computes the conditional support between attributes and generates the Fuzzy Rules considering the relation between them, unlike the traditional C4.5 algorithm, by using the attributes whose conditional support is high. Therefore, because the generated Fuzzy Rules make the number of attributes decreased more than those in the traditional C4.5 algorithm, they make the accuracy of rules improved more. The Fuzzy-based Disease Inference Module (FDIM) not only can reason a patient's disease accurately by using the generated Fuzzy Rules and a patient disease information but also can prevent a patient's disease beforehand.

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2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)

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