Python scikit-fuzzy:开发用于糖尿病诊断的模糊专家系统

Tajul Rosli Razak, Ahmad Zia Ul-Saufie, Mohamad Hanis Yusoff, Mohammad Hafiz Ismail, Shukor Sanim Mohd Fauzi, N. A. Mohd Zaki
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摘要

如今,需要改进糖尿病检测,为患者提供重要信息。这是因为糖尿病已在全球范围内流行,给社会和人民造成了损失。此外,患者往往会误读症状,而临床医生如果收集的数据不足,可能会产生错误的结果。因此,本研究旨在证明,将决定、建议或解决方案等专家意见整合在一起的方案是降低糖尿病发病率的绝佳方法。具体来说,本研究打算实施一个模糊专家系统,该系统可以检测和报告糖尿病的早期阶段,是一种可行的方法。此外,由于该方案人人可用,如果人们拥有血糖监测设备,就可以很容易地进行自我诊断。然而,使用任何编程工具开发针对实际情况(如糖尿病患者)的模糊专家系统并不简单。因此,本研究将提供一种使用流行编程语言 Python 构建模糊专家系统的综合方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Python scikit-fuzzy: developing a fuzzy expert system for diabetes diagnosis
Nowadays, improvements in diabetes detection that provide patients with vital information are needed. This is due to the fact that Diabetes mellitus has generated a worldwide epidemic that costs society and people. Also, patients tend to misread symptoms, and clinicians who collect insufficient data may produce erroneous outcomes. Therefore, this study aims to demonstrate that a programme that integrates expert advice such as decisions, recommendations, or solutions is an excellent method for reducing the incidence of diabetes. Specifically, this study intends to implement a fuzzy expert system that can detect and report the early stages of diabetes as a viable approach. Furthermore, since this programme is available to everyone, people may easily self-diagnose themselves if they have a blood glucose monitoring device. However, developing the fuzzy expert system for real-world situations, such as diabetes patients, using any programming tools is not straightforward. Therefore, this study will provide a comprehensive approach to constructing a fuzzy expert system using the popular programming language Python.
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