CHINESE JOURNAL OF MEDICINAL GUIDE >

2025 , Vol. 27 >Issue 9: 974 - 981

DOI: https://doi.org/magtech.2025.05.12-00002

Study on the Characteristics and Meteorological Triggers of Circulatory System Diseases in Panzhihua Based on Machine Learning

  • Shigong WANG
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  • 1.Inner Mongolia Autonomous Region Meteorological Observatory， Inner Mongolia Huhehaote 010051， China；
    2.Tonglu Meteorological Bureau of Zhejiang Province， Zhejiang Tonglu 311500， China；
    3.Meteorological Medicine Research Center of Panzhihua Central Hospital， Sichuan Panzhihua 617000， China；
    4.Institute of Environmental Meteorology and Health， Chengdu University of Information Technology， Sichuan Chengdu 610225， China

Received date: 2025-05-12

  Revised date: 2025-09-05

  Accepted date: 2025-11-15

  Online published: 2025-11-18

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Abstract

Objective： Study the incidence characteristics and the meteorological triggers of circulatory system diseases in Panzhihua， construct a risk level prediction model， in order to provide disease prevention services for the government，hospitals and other departments， as well as the general public in Panzhihua.Methods： The medical case data certain stage of circulatory system diseases and daily meteorological data during the same period in Panzhihua Central Hospital were collected and organized. Based on the basis of exploring the spectrum and temporal variation characteristics as well as the meteorological triggers of local circulatory system diseases， the prediction model for the risk level of circulatory system diseases in Panzhihua was constructed by the machine learning algorithms， and the prediction effect was tested.Results and Conclusion： The most affected population was the elderly， followed by middle-aged people for the circulatory system diseases in Panzhihua. The number of male patients was about 1.7 times that of females. And the highest number of cases occurred in winter， followed by summer， and the lowest in autumn. January was the peak and october was the valley， that was different from the eastern part in China， the number of outpatients was most significantly affected by the lowest temperatures， with a corresponding increase in the number of outpatients when the lowest temperatures drop to lower. The accuracy of BP forecast model trial forecast was 89.96%. The forecast accuracy of ELMAN forecast model was 93.61%. ELMAN forecasting model was better than BP in forecasting the change trend and series of risk grade，and the model was more stable.


Key words：  ; Circulatory system disease； ; Disease spectrum； ; Meteorological triggers； ; Machine learning； ; Forecasting model

Cite this article

Shigong WANG .

Study on the Characteristics and Meteorological Triggers of Circulatory System Diseases in Panzhihua Based on Machine Learning

[J]. CHINESE JOURNAL OF MEDICINAL GUIDE, 2025 , 27(9) : 974 -981 . DOI: magtech.2025.05.12-00002

References

    ［1］ 姜甲平，王鑫刚.气候变化对人类生存环境的影响分析［J］.中国农业信息，2016，（21）：62.

         ［2］  郑皓月.气候变化对人类生存环境的影响分析［C］.广东教育学会.广东教育学会2024年度学术讨论会暨第十九届广东省中小学校（园）长论坛论文选（四）.辽宁师范大学地理科学学院，2024：5.

         ［3］  宛霞.气候变化正对人类健康造成影响［N］.中国气象报，2022-08-23（003）.

         ［4］  黄存瑞，刘起勇.IPCC AR6报告解读：气候变化与人类健康［J］.气候变化研究进展，2022，18（4）：442-451.

         ［5］  崔兴毅.气候变化带来的威胁正在增加对人类健康的影响［J］.科学大观园，2022，（12）：24-27.

         ［6］  朱毅翔，阚海东.大气环境影响人群健康的研究进展与展望［J］.兰州大学学报（医学版），2025，51（1）：1-11，25.

         ［7］  郭静.9种疾病与极端气候相关［J］.家庭医药·快乐养生，2018，（1）：56.

         ［8］  张成，邓林密.国内外气候变化与健康应对的研究进展［J］.中国医疗管理科学，2019，9（5）：46-52.

         ［9］  Giorgi F，Field C，Barros V .IPCC Climate Change 2013： impacts， adaptation and vulnerability： key findlings and lessons learned； proceedings of the egu general assembly conference［C］.2014.

         ［10］ 陈正洪，杨桂芳，扈海波.气候变化背景下温度对人体健康影响研究进展［J］.中国公共卫生，2014，30（10）：1318-1321.

         ［11］ 雷应朝，胡孟然，龙怀聪，等.攀枝花地区气候康养资源优势及其对呼吸系统疾病影响研究［J］.成都信息工程大学学报，2024，39（2）：223-232.

         ［12］ 赵琦.加强极端天气事件的健康影响研究［J］.环境卫生学杂志，2025，15（6）：465-466，460.

         ［13］ Yang J， Yin P，Zhou M， et al. Cardiovascular mor-tality risk attributable to ambient in China［J］.Heart，2015，101（24）：1966-1972.

         ［14］ 宋雨润，曾胜兰，王式功，等.变温对阜南地区循环系统疾病的影响［J］.成都信息工程大学学报，2023，38（2）：174-180.

         ［15］ 刘明慧.长春市空气污染短期暴露对循环系统疾病住院的影响［D］.吉林大学，2024.WHO. NCD mortality and morbidity ［EB/OL］.（2012-04）［2025-03-16］.http：//www.who.int/gho/ncd/mortality-morbidity/en/index.html/Apri12012.

         ［16］ 魏仁敏，官明德，尹作民，等.心血管内科学［M］.北京：中国科学技术出版社，2007：1-2.

         ［17］ 梅贵琴.改进的Elman神经网络和网络参数优化算法研究［D］.西南大学，2017.

         ［18］ 赵兴赟，张强，杨方社，等.基于XGBoost-SHAP方法的陕西省PM2.5影响因素分析［J/OL］.环境科学研究，1-16［2025-03-16］.https：//doi.org/10.13198/j.issn.1001-6929.2025.03.16.

         ［19］ Braga ALF， Zanobetti A， Schwartz J. The effect of weather on respiratory and cardiovascular deaths in 12 US cities［J］.Environmental Health Perspectives， 2002， 110（9）： 859-863.

         ［20］ Moghadamnia Mohammad Taghi， Ardalan Ali， Mesdaghinia Alireza， et al. Ambient temperature and cardiovascular mortality： a systematic review and meta-analysis［J］.Peer J， 2017， 5：3574.

         ［21］ Huang JX， Wang JF， Yu WW. The lag effects and vulnerabilities of temperature effects on cardiovascular disease mortality in a subtropical climate zone in China［J］.Int J Environmental Res and Public Health， 2014， 11（4）：3982-3994.

         ［22］ 陈美池，牛静萍，阮烨，等.兰州市日均气温与心血管疾病日入院人次的时间序列研究［J］.环境与健康杂志，2014，31（5）：391-394.

         ［23］ Chen J， Zhou M， Yang J， et al. The modifying effects of heat and cold wave characteristics on cardiovascular mortality in 31 major Chinese cities［J］.Environmental Res Letters，2020，15（10）： 105009.

         ［24］ 刘博，党冰，张楠，等.多种气象统计模型对比研究——以气象敏感性疾病脑卒中预报为例［J］.气象与环境学报，2018，34（4）：126-133.

         ［25］ 张莹.我国典型城市空气污染特征及其健康影响和预报研究［D］.兰州大学，2016.

         ［26］ 柳志慧，王式功，赵笑颜.基于机器学习的阜南县呼吸系统和循环系统疾病住院人次预报模型研究［J］.环境与健康杂志，2019，36（10）：871-875，941.

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