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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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模糊量子机器学习 (FQML) 逻辑用于优化疾病预测.

Rabia Khushal¹, Dr Ubaida Fatima¹

¹Department of Mathematics, NED University of Engineering & Technology, Pakistan.

Computers in biology and medicine

|May 8, 2025

概括

本研究介绍了一种混合混量子机器学习 (FQML) 模型,以提高量子机器学习 (QML) 的性能. 在高维度数据中,FQML有效地降低了计算复杂性,以提高医疗预测的准确性.

科学领域:

量子计算是一种量子计算.
机器学习机器学习
人工智能的人工智能

背景情况:

量子机器学习 (QML) 是有前途的,但在高维空间的计算复杂性方面存在困难.
传统的机器学习 (ML) 也面临着对大,高维数据集的准确性和计算时间的问题.

研究的目的:

开发一种新的混合模型,迷糊量子机器学习 (FQML),以解决高维数据中QML的局限性.
提高医疗数据集的准确性和减少计算时间,特别是用于慢性疾病预测.

主要方法:

集成的模糊逻辑 (FL) 概念与量子机器学习 (QML) 算法,包括支持矢量机 (SVM) 和K-Nearest Neighbor (KNN).
将混合FQML模型应用于慢性疾病的药物数据集,通过融合特征来减少维度.
进行统计分析,将FQML与传统的QML进行比较.

主要成果:

与标准QML相比,FQML模型显著优化了计算时间,并提高了准确性.
通过模糊逻辑减少特征,将高维空间转化为低维空间,提高模型性能.
统计分析证实了FQML和QML之间的显著差异,突出了FQML的优势.

结论:

关键词:

慢性疾病慢性疾病慢性疾病迷糊的K-最近的邻居 (FQKNN)模糊的量子机器学习 (FQML)模糊的量子支向量机器 (FQSVM)医疗保健数据集医疗保健数据集

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FQML模型有效地克服了高维空间中的QML的计算复杂性挑战.
通过FQML,可以考虑预测所需的所有特征,而不会损害对医学诊断至关重要的计算效率.
这种混合方法为医疗诊断等领域的复杂数据分析提供了强大的解决方案.