Machine learning approach in mortality rate prediction for hemodialysis patients.

机器学习方法在血液透析患者死亡率预测中的应用.

• 影响指数: 1.669
• 发表时间: Jun 2021 14
• 来源期刊: Comput Methods Biomech Biomed Engin
• DOI: 10.1080/10255842.2021.1937611
• PMID: 34124977
• 文章类型: Journal Article
• 中科院分区: Q3
• 方向: 医学
• 作者列表: Radović N,Prelević V,Erceg M,Antunović T

关键词: K-means clustering algorithm; Mortality rate prediction; chronic kidney disease; support vector machine algorithm

Abstract

Kernel support vector machine algorithm and K-means clustering algorithm are used to determine the expected mortality rate for hemodialysis patients. The national nephrology database of Montenegro has been used to conduct this research. Mortality rate prediction is realized with accuracy up to 94.12% and up to 96.77%, when a complete database is observed and when a reduced database (that contains data for the three most common basic diseases) is observed, respectively. Additionally, it is shown that just a few parameters, most of which are collected during the sole patient examination, are enough for satisfying results.

摘要

采用核支持向量机算法和K-means聚类算法确定血液透析患者的期望死亡率。黑山的国家肾脏病数据库已用于进行这项研究。实现了死亡率预测,准确率达到94.12%,达96.77%,当观察到完整的数据库时，当观察到简化的数据库（包含三种最常见的基础疾病的数据）时，分别。此外,它表明，只有几个参数，其中大部分是在唯一的患者检查期间收集的，足以满足结果。