量子灵感引力引导的粒子群集优化,用于特征选择和分类
Saleem Malik1, S Gopal Krishna Patro2, Chandrakanta Mahanty3
1CSE Department, P A College of Engineering, 574153, Coimbatore, India. baronsaleem@gmail.com.
Scientific reports
|October 1, 2025
概括
一个新的量子灵感引力引导粒子群集优化 (QIGPSO) 通过有效地选择关键医疗数据特征来改善非传染性疾病的诊断. 这种方法提高了准确性,并帮助医生做出更好的治疗决策.
科学领域:
- 计算智能是一种计算智能.
- 医疗信息学 医疗信息学
- 优化算法 优化算法
背景情况:
- 基于人口的元启发算法平衡了复杂问题的探索和利用.
- 像遗传算法,粒子群优化和引力搜索算法这样的现有方法面临着诸如过早收和参数灵敏度等局限性.
- 准确诊断非传染性疾病 (NCD) 对于有效的患者治疗至关重要.
研究的目的:
- 为复杂的优化挑战引入量子启发引力引导粒子集群优化 (QIGPSO).
- 在医学数据分析中使用先进的元启发式优化来增强非传染性疾病 (NCD) 的诊断.
- 利用量子粒子群集优化 (QPSO) 和引力搜索算法 (GSA) 的优势,改善搜索流程.
主要方法:
- 通过整合QPSO和GSA开发了QIGPSO,以利用全球和本地搜索能力.
- 实现了绝对的高斯随机变量,并修改了位置更新方程,以改进搜索机制.
- 使用基于包装的方法与支持矢量机 (SVM) 进行NCD数据集的特征选择和分类.
主要成果:
- QIGPSO在识别NCD诊断医疗数据集中的关键特征方面表现出有效性.
- 该算法实现了高准确率,并在各种NCD数据集中减少了错误分类数量.
- 与传统的优化方法相比,QIGPSO表现出更快的趋同,改善了勘探开发平衡.
结论:
- 在NCD医疗数据分析中,QIGPSO提供了一种强大而高效的特征选择和分类方法.
- 增强优化技术提供了宝贵的数据洞察力,支持临床医生做出明智的治疗决策.
- QIGPSO有效地解决了传统算法的局限性,为医疗保健中的改进诊断工具铺平了道路.
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