Thyroid Disease Prediction Based on Improved Support Vector Regression Algorithm

GUO Wei; LI Lian-di; YU Guang-hao

doi:10.3969/j.issn.2096-6091.2023.13.001

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Thyroid Disease Prediction Based on Improved Support Vector Regression Algorithm

RESEARCH PAPER | 更新时间：2025-03-26

- Thyroid Disease Prediction Based on Improved Support Vector Regression Algorithm
- New Generation of Information Technology Vol. 6, Issue 13, Pages: 1-5(2023)
- 作者机构：
  
  1.牡丹江医学院生命学院，黑龙江牡丹江 157011
  2.牡丹江医学院附属第二医院，黑龙江牡丹江 157011
  3.牡丹江医学院医学影像学院，黑龙江牡丹江 157011
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.2096-6091.2023.13.001
  CLC： R737.25
- Accepted：2023-06-20，
  
  Published：15 July 2023
- 稿件说明：
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国威, 李莲娣, 于广浩. 基于改进支持向量回归算法的甲状腺疾病预测[J]. 新一代信息技术, 2023, 6(13): 01-05

GUO Wei, LI Lian-di, YU Guang-hao. Thyroid Disease Prediction Based on Improved Support Vector Regression Algorithm[J]. New Generation of Information Technology, 2023, 6(13): 01-05
国威, 李莲娣, 于广浩. 基于改进支持向量回归算法的甲状腺疾病预测[J]. 新一代信息技术, 2023, 6(13): 01-05 DOI： 10.3969/j.issn.2096-6091.2023.13.001.

GUO Wei, LI Lian-di, YU Guang-hao. Thyroid Disease Prediction Based on Improved Support Vector Regression Algorithm[J]. New Generation of Information Technology, 2023, 6(13): 01-05 DOI： 10.3969/j.issn.2096-6091.2023.13.001.

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0.270 9，

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。

Abstract

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 0.270 9

is 0.744

and the prediction accuracy of the mod

el is 0.926

indicating good prediction accuracy and fast convergence speed.

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Keywords

references

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