Abdel-Halim R E, Abdel-Aal R E
Department of Surgery, King Khalid University Hospital, Riyadh, Saudi Arabia.
Comput Methods Programs Biomed. 1999 Jan;58(1):69-81. doi: 10.1016/s0169-2607(98)00075-3.
The cluster analysis technique is considered for classifying kidney stones based on data for nine chemical analysis parameters. A set of 214 stones is used, which has been previously classified using empirical classification rules into three stone types using the percentage concentrations of the urate, oxalate, and phosphate radicals. We investigate whether cluster analysis utilising data on all parameters leads to different classifications and explore the possibility of other effective classifiers. We also compare the performance of various clustering techniques, distance and similarity measures and data standardisation methods. Results indicate that inclusion of the additional six parameters does not improve the classification accuracy. Best matching with the empirical classification (6% error) is achieved using the average linkage (between groups) clustering method and the squared Eculidean distance measure without data standardisation. Excluding these three main radicals causes a 63% matching error. Cluster analysis results suggest that carbon ions alone provide a single classifier for the three stone types, giving a matching error of approximately 10% with the empirical classification.
考虑采用聚类分析技术，基于九个化学分析参数的数据对肾结石进行分类。使用了一组214颗结石，这些结石先前已根据尿酸盐、草酸盐和磷酸盐基团的百分比浓度，采用经验分类规则分为三种结石类型。我们研究利用所有参数的数据进行聚类分析是否会导致不同的分类，并探索其他有效分类器的可能性。我们还比较了各种聚类技术、距离和相似性度量以及数据标准化方法的性能。结果表明，纳入额外的六个参数并不能提高分类准确性。在不进行数据标准化的情况下，使用平均连锁（组间）聚类方法和平方欧几里得距离度量可实现与经验分类的最佳匹配（误差为6%）。排除这三种主要基团会导致63%的匹配误差。聚类分析结果表明，仅碳离子就为三种结石类型提供了单一分类器，与经验分类的匹配误差约为10%。
