使用FA-GA-BP模型和人均家庭支出预测家庭医生合同履行倾向
1School of Economics and Management, Beijing Jiaotong University, Beijing, China.
Frontiers in public health
|January 26, 2026
概括
一个新的模型准确地预测了社区居民的家庭医生合同服务 (FDCS) 履行情况. 该工具通过因子分析和基因算法优化的反向传播神经网络来加强对基本卫生服务质量的监测.
科学领域:
- 社区卫生服务研究研究
- 医疗信息学 医疗信息学
- 在医疗保健中的预测建模.
背景情况:
- 监测家庭医生合同服务 (FDCS) 对社区健康至关重要.
- 开发准确的FDCS履行预测模型是一个关键的挑战.
- 现有的方法缺乏有效质量评估所需的精度.
研究的目的:
- 确定影响城市社区FDCS履行倾向的因素.
- 开发一个高精度,低误差的反转模型来预测FDCS的实现.
- 加强对FDCS质量的监测,并推进社区卫生服务.
主要方法:
- 利用城市社区的家庭生活费用人均调查数据.
- 结合因素分析 (FA) 与遗传算法 (GA) 优化了反向传播 (BP) 神经网络.
- 确定了影响FDCS履行的"生活质量"和"刚性需求"因素.
主要成果:
- 在FA-GA-BP模型中,通过测量值获得0.9223的确定系数 (R^2).
- 根平均平方误差和相对误差分别为0.0618和9.20%.
- 对分类社区的模拟预测超过了0.8138.8的确定系数.
结论:
- 该FA-GA-BP模型为FDCS实现提供可靠和可概括的预测.
- 该模型显示了高精度,低误差,以及跨多种社区数据集的兼容性.
- 结果支持FDCS履行状态的动态监测,以改善医疗保健管理.
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