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基于L-S-ACO算法医学诊断模型的特征选择研究

Ping Wang¹, Boyuan Tan¹, Yansong Fu¹

¹School of Medical Information, Changchun University of Chinese Medicine, No. 1035 Boshuo Road, Changchun 130117, China.

|March 9, 2026

概括

通过提高特征选择准确度,L-S-ACO算法增强了医疗诊断. 这种新的方法,将群优化与LightGBM和SAG相结合,提高了诊断模型的性能和可靠性.

科学领域:

机器学习机器学习
医疗信息学医疗信息学
计算智能是一种计算智能.

背景情况:

医学诊断中的特征选择需要解决特征相互依赖.
群优化 (ACO) 对于特征选择和相关性挑战是有效的.
当前的ACO方法在与其他算法集成时显示出更好的性能.

研究的目的:

开发一种新的算法,L-S-ACO,用于医疗诊断模型中的特征选择.
用综合方法解决医疗数据中的特征相关性问题.
基于拟议的L-S-ACO算法评估诊断模型的性能.

主要方法:

该研究介绍了L-S-ACO算法,将群优化 (ACO) 与光梯度增强机 (LightGBM) 和随机平均梯度 (SAG) 结合起来.
使用L-S-ACO算法开发了一种医学诊断模型.
分析了该模型在疾病诊断中的实际应用和性能.

主要成果:

L-S-ACO算法在诊断模型性能上显示出显著的改进.
精度 (ACC) 增加了5.81%,F1得分增加了6.21%,曲线下的面积 (AUC) 增加了4.08%.
与基线方法相比,精度提高了5.12%,回忆提高了6.35%.

结论:

拟议的L-S-ACO算法有效地处理医疗数据中的特征相互依赖性和相关性.
开发的诊断模型显示了疾病诊断的提高准确性和可靠性.
这种综合方法为医学诊断建模提供了有前途的进步.

关键词:

卫生科学卫生科学

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