摘要
为了对医疗财务领域产生的海量数据进行识别和趋势预测,文中提出了一种基于改进岭回归的数据识别与预测算法。模型利用具有自注意力机制的卷积神经网络对数据的特征进行提取,并分配相应的权重,然后通过改进的岭回归算法进行风险预测。在岭回归算法中引入核函数,将非线性数据映射到高维空间实现数据的精准回归预测。以6家大型医疗机构的数据集为样本进行实验,结果表明,所提模型算法的预测精确率达95.4%,召回率为94.6%,F1分数为94.8%,高于传统的主流机器学习算法模型,证明了该算法模型的有效性,能够对数据进行风险预测。
In order to identify and predict the massive data generated in the field of medical finance,this paper proposes a data recognition and prediction algorithm based on improved ridge regression.The model utilizes a convolutional neural network with self attention mechanism to extract the features of the data,assign corresponding weights,and then use an improved ridge regression algorithm for financial risk prediction.The kernel function is introduced into the ridge regression algorithm to map nonlinear data into high-dimensional space for accurate regression prediction of data.The experiment results using datasets from six large medical institutions as samples show that the proposed model algorithm has a prediction accuracy of 95.4%,a recall rate of 94.6%,and an F1 score of 94.8%,which is higher than traditional mainstream machine learning algorithm models.This proves the effectiveness of the algorithm model and its ability to predict risks in medical financial data.
作者
马骁
MA Xiao(The First Affiliated Hospital of Hebei North University,Zhangjiakou 075000,Hebei Province,China)
出处
《信息技术》
2025年第3期23-27,共5页
Information Technology
基金
河北省自然科学基金项目(H2021405008)。
关键词
医疗数据分析
岭回归
卷积神经网络
识别预测
数据映射
medical data analysis
ridge regression
convolutional neural network
identification and prediction
data mapping
作者简介
马骁(1992-),女,本科,助理会计师,研究方向为财务数据分析。
