Nursing Diagnosis of Urology Operating Room Based on New Association Classification Algorithm.
Nursing Diagnosis of Urology Operating Room Based on New Association Classification Algorithm.
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DOI：
10.1155/2022/4674959
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发表时间：
2022
影响因子：
--
通讯作者：
Zhang, Hongyan
中科院分区：
文献类型：
--
作者：
Zhang, Hongyan
由于医学工程的快速发展，各种医疗仪器记录和保存了大量的数据。因此，寻找数据之间的关系，总结临床表现，对各种疾病的诊断、治疗和医学研究具有重要意义。研究泌尿外科手术室护理诊断支持系统的关键是选择适合泌尿外科疾病特点的有效分类算法。首先，我们通过医学数据挖掘分析了泌尿系统疾病的特点。其次，在传统数据挖掘分类方法和泌尿外科疾病诊断研究的基础上，介绍了泌尿外科疾病实验源数据集，分析了疾病特征。介绍了决策树（包括ID3、C4.5）、贝叶斯分类、BP神经网络、关联规则分类等分类算法和步骤。利用这些算法在泌尿系统疾病数据集上进行了相关的对比实验。最后，以泌尿系统疾病的诊断为例，提出了一种基于频繁闭项集的关联分类算法（ACCF），沿着给出了相应的解释。为了验证算法的可操作性，用C++语言实现了所提算法，并与传统关联分类算法和数据挖掘方法的分类效果进行了比较。理论分析和实验结果均表明，该算法解决了现有数据挖掘算法的各种缺陷，同时提高了泌尿系统疾病分类和预测的准确性。
Due to the rapid development of medical engineering, massive amounts of data are recorded and preserved by various medical instruments. Therefore, finding relationships among data and summarizing clinical manifestations are of great significance to the diagnosis, treatment, and medical research of various diseases. The key to studying the nursing diagnosis support system, particularly in the urological operating room, is to select an effective classification algorithm, which is suitable for the characteristics of urological diseases. Initially, we have analyzed characteristics of urological diseases through medical data mining. Secondly, based on the traditional data mining classification method and urological disease diagnosis research, we have introduced the urological disease experimental source dataset and analyzed characteristics of the disease. Furthermore, classification algorithm and steps were introduced such as decision tree (including ID3, C4.5), Bayesian classification, BP neural network, and association rule classification algorithms. These algorithms are used to make relevant comparative experiments on the urological disease dataset. Finally, based on the diagnosis of urological diseases, a new association classification algorithm (ACCF), which is based on frequent closed item sets, is proposed along with suitable explanation. In order to verify the operational capabilities, the proposed algorithms are implemented in C++ and compared with the classification effect of traditional association classification algorithms and data mining methods. Both theoretical analysis and experiment results show that the proposed algorithm has resolved various deficiencies of the existing data mining algorithms and equally improved the accuracy of urological disease classification and prediction.
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DOI：
10.1016/0168-5597(92)90080-u
发表时间：
1992-04-01
期刊：
ELECTROENCEPHALOGRAPHY AND CLINICAL NEUROPHYSIOLOGY
影响因子：
--
作者：
ANDREASSEN, S;FALCK, B;OLESEN, KG
通讯作者：
OLESEN, KG
影响因子：
2.7
作者：
Wu, Xindong;Kumar, Vipin;Steinberg, Dan
通讯作者：
Steinberg, Dan
影响因子：
4.6
作者：
Parker, RS;Doyle, FJ;Peppas, NA
通讯作者：
Peppas, NA
影响因子：
6
作者：
Pei, Jiaming;Zhong, Kaiyang;Wang, Xinyi
通讯作者：
Wang, Xinyi
影响因子：
158.5
作者：
MILLER, RA;POPLE, HE;MYERS, JD
通讯作者：
MYERS, JD