基于机器学习的山西省人间布鲁氏菌病的预测研究  

Prediction of Human Brucellosis in Shanxi Province Based on Machine Learning

作　　者：张珍珍[1] 阮若楠 苑宏泽 张龙 ZHANG Zhenzhen;RUAN Ruonan;YUAN Hongze;ZHANG Long(Department of Computer Science and Technology,Taiyuan University,Taiyuan 030032,China)

机构地区：[1]太原学院计算机科学与技术系,山西太原030032

出　　处：《太原学院学报（自然科学版）》2022年第4期65-71,共7页Journal of TaiYuan University:Natural Science Edition

基　　金：山西省基础研究计划资助项目(20210302124608)。

摘　　要：布鲁氏菌病是一种人畜共患疾病,近年来山西省布鲁氏菌病的患病人数逐渐增多,且流行性强度大,发病范围较广,所以对于人间布鲁氏菌病的预测以及防控非常重要。从公共卫生数据中心官方网站收集了2010-2020年山西省人间布鲁氏菌病月发病信息,利用气象因素构建基于支持向量机、随机森林和深度神经网络3种机器学习模型的人间布鲁氏菌病发病率预测模型,用以预测并评估山西省人间布病疫情的变化趋势。结果表明气象因素与人间布病发病率存在显著相关性,且随机森林模型的预测最为准确。Brucellosis is a zoonotic disease.In recent years,both the prevalence and incidence of human brucellosis among Shanxi people have increased,so the prediction,prevention and control of the infectious disease have become vitally important.The data concerning the monthly incidence of human brucellosis in Shanxi were obtained from the official website of Public Health Science Data Centre.Based on three machine learning methods—support vector machine,random forest and neural network,prediction models were constructed using meteorological factors for the prediction and assessment of the change trend of human brucellosis epidemic in Shanxi Province.The results showed that there was a significant correlation between meteorological factors and the incidence of human brucellosis,and that the prediction of random forest method was the most accurate among these three algorithms.

关 键 词：人间布病 机器学习 气象因素 预测 

分 类 号：TP311.1[自动化与计算机技术—计算机软件与理论]