使用数据挖掘技术预测慢性肾脏疾病:一项回顾性研究。

IF 1.7 Q2 MEDICINE, GENERAL & INTERNAL
International Journal of Preventive Medicine Pub Date : 2023-08-28 eCollection Date: 2023-01-01 DOI:10.4103/ijpvm.ijpvm_482_21
Mohammad Sattari, Maryam Mohammadi
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引用次数: 0

摘要

慢性肾脏病(CKD)是全球日益严重的健康问题之一。慢性肾脏疾病的早期诊断、控制和管理非常重要。这项研究考虑了2016年至2021年间发表的使用分类方法预测肾脏疾病的英文文章。数据挖掘模型在预测疾病方面发挥着至关重要的作用。通过我们的研究,支持向量机、朴素贝叶斯和k近邻的数据挖掘技术的频率最高。之后,随机森林、神经网络和决策树成为最常见的数据挖掘技术。在与慢性肾脏疾病相关的风险因素中,白蛋白、年龄、红细胞、脓细胞和血清肌酐的风险因素在这些研究中的频率最高。最高数量的最佳产量被分配给随机森林技术。审查肾脏疾病领域的大型数据库有助于更好地分析疾病,并确保提取出风险因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Using Data Mining Techniques to Predict Chronic Kidney Disease: A Review Study.

Using Data Mining Techniques to Predict Chronic Kidney Disease: A Review Study.

One of the growing global health problems is chronic kidney disease (CKD). Early diagnosis, control, and management of chronic kidney disease are very important. This study considers articles published in English between 2016 and 2021 that use classification methods to predict kidney disease. Data mining models play a vital role in predicting disease. Through our study, data mining techniques of support vector machine, Naive Bayes, and k-nearest neighbor had the highest frequency. After that, random forest, neural network, and decision tree were the most common data mining techniques. Among the risk factors associated with chronic kidney disease, respectively, risk factors of albumin, age, red blood cells, pus cells, and serum creatinine had the highest frequency in these studies. The highest number of best yields was allocated to random forest technique. Reviewing larger databases in the field of kidney disease can help to better analyze the disease and ensure the risk factors extracted.

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来源期刊
International Journal of Preventive Medicine
International Journal of Preventive Medicine MEDICINE, GENERAL & INTERNAL-
CiteScore
3.20
自引率
4.80%
发文量
107
期刊介绍: International Journal of Preventive Medicine, a publication of Isfahan University of Medical Sciences, is a peer-reviewed online journal with Continuous print on demand compilation of issues published. The journal’s full text is available online at http://www.ijpvmjournal.net. The journal allows free access (Open Access) to its contents and permits authors to self-archive final accepted version of the articles on any OAI-compliant institutional / subject-based repository. The journal will cover technical and clinical studies related to health, ethical and social issues in field of Preventive Medicine. Articles with clinical interest and implications will be given preference.
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