The input data set includes 729 objects (patients) and 39 variables (clinical qualitative and quantitative descriptors).

For reliable data treatment and interpretation qualitative descriptors were omitted and only numerical clinical indicators were included in the data matrix. Finally, the data set dimension was [729 x 18]. The data were treated by hierarchical cluster analysis and factor analysis. The major goal of the data mining was to reach statistically significant partitioning of the objects and variables into similarity patterns (clusters) which helps to better understand the data structure, to assess the meaning of the partitioning achieved, thus promoting the evaluation of the health status of the patients and the role of specific descriptors for the formation of the partitioning patterns. 3D classification Python tool.为保障数据处理与解读的可靠性，本研究剔除了定性描述符，仅将数值型临床指标纳入数据矩阵。最终该数据集的维度为[729 × 18]。数据采用分层聚类分析与因子分析进行处理。本次数据挖掘的核心目标是实现对象与变量的统计学显著性分组，将其划分为相似性模式（即聚类簇），以此助力更深入地理解数据结构、评估所得分组的意义，进而推动患者健康状态评估以及特定描述符在分组模式形成中所起作用的研究。本研究配套3D分类Python工具。