基于变量量子分类器的慢性病早期识别和分类,使用稀疏的自编码器和LASSO收缩
P Parthasarathi1, Haya Mesfer Alshahrani2, K Venkatachalam3
1Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Erode, India.
PeerJ. Computer science
|June 26, 2025
概括
本研究引入了一种量子机器学习 (QML) 方法,用于早期慢性病 (CKD) 诊断. 新的QML模型达到99.2%的准确性,超过了识别这种严重疾病的传统方法.
科学领域:
- 医疗信息学 医疗信息学
- 量子计算是一种量子计算.
- 机器学习 机器学习
背景情况:
- 慢性病 (CKD) 是导致过早死亡的主要原因,主要是高血压和糖尿病.
- 目前的诊断方法依赖于膜过率和炎症生物标志物,往往在晚期识别疾病.
- 及时诊断至关重要,以减轻与CKD相关的严重影响和高死亡率.
研究的目的:
- 开发和评估基于量子机器学习 (QML) 的技术,用于慢性病 (CKD) 的早期诊断和预后.
- 与传统分类方法相比,提高CKD检测的准确性和效率.
主要方法:
- 这项研究采用了四个阶段的方法:数据预处理 (卡尔曼波器,规范化),数据增强 (零散自动编码器),特征选择 (LASSO收缩) 和分类 (变量量子分类器).
- 拟议的系统在UCI数据集上得到了验证,其中包含400名早期CKD患者,具有25个属性.
主要成果:
- 基于QML的系统在分类慢性病方面表现出卓越的表现.
- 实现了99.2%的分类准确度,超过了传统分类器.
- 评估指标包括F1分数,精度,回忆和准确性.
结论:
- 拟议的量子机器学习模型显示出对慢性病的准确和早期检测具有重大前景.
- 这种QML方法在诊断和管理CKD方面提供了潜在的进步,可能降低死亡率.
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