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Probability Analysis of Hypertension-Related Symptoms Based on XGBoost and Clustering Algorithm

التفاصيل البيبلوغرافية
العنوان: Probability Analysis of Hypertension-Related Symptoms Based on XGBoost and Clustering Algorithm
المؤلفون: Wenbing Chang, Yinglai Liu, Yiyong Xiao, Xingxing Xu, Shenghan Zhou, Xuefeng Lu, Yang Cheng
المصدر: Applied Sciences, Vol 9, Iss 6, p 1215 (2019)
بيانات النشر: MDPI AG, 2019.
سنة النشر: 2019
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: hypertension, cluster analysis, XGBoost algorithm, hypertension related symptoms, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: In this paper, cluster analysis and the XGBoost method are used to analyze the related symptoms of various types of young hypertensive patients, and finally guide patients to target treatment. Hypertension is a chronic disease that is common worldwide. The incidence of it is increasing, and the age level of patients is decreasing year by year. Effective treatment of youth hypertension has become a problem in the world. In this paper, young hypertension patients are classified into two groups by cluster analysis; the proportion of different hypertension related symptoms in each group of patients is then counted; and after verifying the prediction accuracy of the XGBoost model with 10-fold cross-validation, the accuracy of clustering is calculated by the XGBoost method. The final result shows that there are significant differences in symptomatic entropy between patients with type II hypertension and those with type I hypertension. Patients with type II hypertension are more likely to have symptoms of ventricular hypertrophy and microalbuminuria. Through this analysis, patients can have preventive treatment according to their own situation, and this can reduce the burden of medical expenses and prevent major diseases. Applying the data analysis into the medical field has great practical significance.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/9/6/1215; https://doaj.org/toc/2076-3417
DOI: 10.3390/app9061215
URL الوصول: https://doaj.org/article/53565b64f2e3402ba7f64da8b5ae1885
رقم الأكسشن: edsdoj.53565b64f2e3402ba7f64da8b5ae1885
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app9061215