摘要
本文构建一种甲状腺疾病机器学习预测诊断算法模型。从UCI(University of California,Irvine)网站获取甲状腺疾病数据集并进行预处理。引入鲸鱼优化算法优化支持向量回归(Support Vector Regression,SVR)模型参数,构建甲状腺疾病预测模型。基于改进支持向量回归算法预测模型能够准确的预测甲状腺功能状况,优化后模型均方误差根为0.2709,R2为0.744,模型预测准确率为0.926,具有较好的预测精度和较快的收敛速度。
To construct a machine learning predictive diagnostic algorithm model for thyroid diseases,this paper obtains a thyroid disease dataset from the UCI(University of California,Irvine)website and preprocesses it.It introduces the whale optimization algorithm to optimize the parameters of the least squares support vector machine model and constructs a thyroid disease prediction model.Based on the improved support vector regression algorithm,the prediction model can accurately predict thyroid function status.After optimization,the root mean square error of the model is 0.2709,R2 is 0.744,and the prediction accuracy of the model is 0.926,indicating good prediction accuracy and fast convergence speed.
作者
国威
李莲娣
于广浩
GUO Wei;LI Lian-di;YU Guang-hao(School of Life,Mudanjiang Medical College,Mudanjiang,Heilongjiang 157011,China;Second Affiliated Hospital of Mudanjiang Medical College,Mudanjiang,Heilongjiang 157011,China;School of Medical Imaging,Mudanjiang Medical College,Mudanjiang,Heilongjiang 157011,China)
出处
《新一代信息技术》
2023年第13期1-5,共5页
New Generation of Information Technology
基金
国家自然联合基金项目(No.U22A20350)
牡丹江医学院科学基金火炬计划(No.2022-MYHJ-008)
牡丹江医学院导师科研专项计划(No.YJSZX2022136)
关键词
支持向量回归
鲸鱼优化
最小二乘支持向量机
甲状腺疾病
预测模型
support vector regression
whale optimization
least squares support vector machine
thyroid diseases
prediction model Citation:
