远程医疗中患者数据处理的模糊遗传算法

Richa Gupta, Parmod Kumar
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引用次数: 1

摘要

从远处接收到的患者数据总是存在不确定性和模糊性。因此,不可能正确地提取有关患者病情的信息,并且患者数据需要在呈现给医生之前进行处理。本文证明了在医生/专家医生端使用模糊遗传算法处理患者数据可以减少患者数据中的不确定性和模糊性。医生能够更可靠地诊断病人的疾病,并据此开药。遗传算法的初始种群是用假设的模糊函数随机生成的。这些功能利用繁殖、交叉和变异理论进行优化。结果表明,模糊遗传算法对患者数据的处理效果令人满意。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy-Genetic Algorithm for Patient Data Processing in Telemedicine
Uncertainties and vagueness are always present in the patient data received from far distance. It is, therefore, not possible to extract the information about the patient condition properly, and the patient data need processing before presenting to the physician. In this paper, it has been demonstrated that processing the patient data with a fuzzy-genetic algorithm at physician/expert doctor end will reduce the uncertainties and vagueness in the patient data. The physician is able to diagnose the patient disease with better reliability and prescribe the medicine accordingly.. Initial population for the genetic algorithm is randomly generated with assumed fuzzy functions. These functions are optimized using the theory of reproduction, crossover, and mutation. The result shows that the fuzzy-genetic algorithm gives satisfactory result for processing the patient data for the purpose.
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