使用模糊专家系统准确预测亚洲国家的焦虑水平
Mouz Ramzan1, Muhammad Hamid2, Amel Ali Alhussan3
1Department of Computer Science, National College of Business Administration and Economics (NCBA&E), Lahore 54000, Pakistan.
Healthcare (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一个使用模糊推理 (FIS) 检测身体症状的焦虑水平的专家系统. 该系统实现了87%的准确性,为早期检测焦虑障碍提供了可靠的工具.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 心理健康技术 心理健康技术
背景情况:
- 焦虑是一种广泛的心理健康状况,具有显著的身体和心理影响.
- 目前用于焦虑的诊断方法可能是主观的,突出了客观工具的需要.
- 早期发现焦虑对于有效的管理和治疗至关重要.
研究的目的:
- 开发一个客观可靠的专家系统,用于早期检测焦虑水平.
- 用身体症状作为预测焦虑的输入变量.
- 为了解决诊断焦虑障碍的固有复杂性和不确定性.
主要方法:
- 实施一个模糊的推断系统 (FIS) 作为一个专家系统.
- 整合了一组全面的输入变量,代表身体症状.
- 开发一个基于规则的系统,编码有关焦虑障碍的医学知识.
主要成果:
- 基于FIS的专家系统在预测焦虑水平方面表现出很高的准确性.
- 在现实世界数据集 (包括来自巴基斯坦的数据集) 上进行测试,获得了87%的值得注意的准确性.
- 该系统有效地处理与焦虑相关的不精确性和不确定性.
结论:
- 开发的基于FIS的专家系统为客观的焦虑水平评估提供了强大的方法.
- 这种工具可能有助于临床医生更有效地诊断和治疗焦虑症.
- 该系统为改善焦虑障碍管理提供了一个有希望的途径,特别是在缺乏有效补救措施的地方.
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