使用机器学习算法预测慢性阻塞性肺疾病的分期

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
I. Mohamed
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引用次数: 0

摘要

确定慢性阻塞性肺疾病(COPD)的严重程度对控制相关死亡率和降低相关费用具有重要意义。本研究旨在建立COPD分期预测模型,并比较五种机器学习算法的相对性能,以确定最优预测算法。这项研究基于从埃及一家私立医院收集的2018年和2019年两个日历年的数据。使用五种机器学习算法进行比较。F1评分、特异性、敏感性、准确性、阳性预测值和阴性预测值是算法比较的性能指标。分析包括211例患者的记录。我们的研究结果表明,在大多数疾病阶段中表现最好的算法是具有最佳预测精度的PNN,因此它可以被认为是决策者预测COPD严重程度阶段的有力预测工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Chronic Obstructive Pulmonary Disease Stages Using Machine Learning Algorithms
Identifying chronic obstructive pulmonary disease (COPD) severity stages is of great importance to control the related mortality rates and reduce the associated costs. This study aims to build prediction models for COPD stages and, to compare the relative performance of five machine learning algorithms to determine the optimal prediction algorithm. This research is based on data collected from a private hospital in Egypt for the two calendar years 2018 and 2019. Five machine learning algorithms were used for the comparison. The F1 score, specificity, sensitivity, accuracy, positive predictive value and negative predictive value were the performance measures used for algorithms comparison. Analysis included 211 patients’ records. Our results show that the best performing algorithm in most of the disease stages is the PNN with the optimal prediction accuracy and hence it can be considered as a powerful prediction tool used by decision makers in predicting severity stages of COPD.
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来源期刊
International Journal of Decision Support System Technology
International Journal of Decision Support System Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.20
自引率
18.20%
发文量
40
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