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A comparison of effectiveness of risk data clustering method in Psychiatric Patient Service

机译:风险数据聚类方法在精神病患者服务中的有效性比较

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In this paper, we clustered clinical risk data of a mental health service, Khon Kaen Rajanagarindra Psychiatric Hospital. This study aims to compare performance values of cluster (k) in k-means clustering algorithm and hierarchical clustering algorithm. The result shows that for k-means clustering algorithm, sum of squared error (SSE) is 32.68, minimum of distance (MD) is 1.38, mean squared error (MSE) is 2.95 and values of k is 11. Therefore, we found that k-means clustering algorithm is the most appropriate method for using in cluster the risk group of the Psychiatric Patient Service. The result also suggests that the most risky age is between the ages of 32 and 36. The result can be a guideline for further research about data prediction. The implications of this study can assist medical staff to be knowledgeable about what should beware of when they treat psychiatric patients and this can be basic planning medicate guidelines for medical staff.
机译:在本文中,我们聚集了精神卫生服务的临床风险数据,Khon Kaen Rajanagarindra精神病院。本研究旨在比较K-Means聚类算法和分层聚类算法中群集(k)的性能值。结果表明,对于k-means聚类算法,平方误差(SSE)的总和是32.68,距离(MD)的最小值为1.38,平均方形误差(MSE)为2.95,K为11.因此,我们发现K-Means聚类算法是最合适的方法在集群中使用精神病患者服务的风险组。结果还表明,最大的年龄在32和36岁之间。结果可以是进一步研究数据预测的指导。本研究的含义可以帮助医务人员了解应该适用于治疗精神病患者时应该是什么,这可能是医务人员的基本规划准则。

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