通过增强的进化计算和集体学习来预测皮肤疾病的患者访问量
Wenting Leng1, Chenglin Yang2, Menggang Kou3
1The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, 730000, China.
Journal of medical systems
|April 23, 2025
概括
这项研究开发了一种增强的机器学习模型,以准确预测每日皮肤病外科医生对等皮肤疾病的诊断. 该模型显著提高了预测准确度,有助于医疗保健资源优化.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 时间序列分析时间序列分析
背景情况:
- 皮肤疾病对全球健康构成重大负担,需要有效管理医疗保健资源.
- 精确预测皮肤病门诊访问对于优化医院运作和患者护理至关重要.
- 现有的预测模型往往在准确性和适应性方面面临限制.
研究的目的:
- 开发一种最佳的预测模型,用于每日皮肤病门诊访问,使用组合学习和增强的优化算法.
- 为了提高皮肤病患者预测量的准确性和可靠性.
- 提供数据驱动的方法,以改善皮肤病学部门的医疗保健资源配置.
主要方法:
- 将四个神经网络模型集成到一个集体学习框架中.
- 开发了一种增强的沙猫群优化 (SCSO) 算法,结合混乱映射,螺旋搜索和警告机制.
- 应用增强的SCSO来优化变量模式分解 (VMD) 用于时间序列模式提取和组合模型重量确定.
主要成果:
- 拟议的整体模型实现了4.43的根平均平方误差 (RMSE) 和0.98的R平方 (R2) 预测门诊访问.
- 与单个模型相比,观察到显著改善,RMSE减少了79.69%,R2增加了36.97%.
- 增强的SCSO有效优化了VMD参数和组合模型重量,证明了卓越的性能.
结论:
- 开发的整体模型,通过增强的SCSO进行优化,为预测皮肤病门诊访问提供了非常有效的方法.
- 这种方法提高了医疗时间序列预测的准确性,并证明了优化医疗保健资源配置的潜力.
- 该研究强调了将先进的机器学习技术结合起来,用于管理诸如皮肤病等公共卫生挑战的好处.
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