Department of Information sciences and Technology, Yanshan University, Qinhuangdao 066000, China.
Int J Environ Res Public Health. 2019 May 6;16(9):1581. doi: 10.3390/ijerph16091581.
The grouping of clusters is an important task to perform for the initial stage of clinical implication and diagnosis of a disease. The researchers performed evaluation work on instance distributions and cluster groups for epidemic classification, based on manual data extracted from various repositories, in order to evaluate Euclidean points. This study was carried out on Weka (3.9.2) using 281 real-life health records of diabetes mellitus patients including males and females of ages>20 and <87, who were simultaneously suffering from other chronic disease symptoms, in Nigeria from 2017 to 2018. Updated plugins of K-mean and self-organizing map(SOM) machine learning algorithms were used to cluster the data class of mellitus type for initial clinical implications. The results of the K-mean assessment were built in 0.21 seconds with nine iterations for "type" and eight for "class" attributes. Out of 281 instances, 87 (30.97%) were classified as negative and 194 (69.03%) as positive in the testing on the Euclidean space plot. By assessment for Euclidean points, SOM discovered the search space in a more effective way, but K-mean positioning potencies are impulsive in convergence. This study is important for epidemiological disease diagnosis in countries with a high epidemic risk and low socioeconomic status.
聚类分组是进行疾病临床意义和诊断初始阶段的一项重要任务。研究人员基于从各种存储库中提取的手动数据，对传染病分类的实例分布和聚类组进行了评估，以评估欧几里得点。这项研究是在 Weka（3.9.2）上进行的，使用了 2017 年至 2018 年间尼日利亚 281 名年龄在 20 岁以上和 87 岁以下的男性和女性糖尿病患者的真实健康记录，这些患者同时患有其他慢性疾病症状。使用了更新的 K-均值和自组织映射（SOM）机器学习算法插件来聚类糖尿病类型的数据类，以进行初步临床意义评估。K-均值评估的结果在 0.21 秒内构建，有 9 次迭代用于“类型”属性，8 次迭代用于“类”属性。在欧几里得空间图上的测试中，281 个实例中有 87 个（30.97%）被归类为阴性，194 个（69.03%）被归类为阳性。通过对欧几里得点的评估，SOM 以更有效的方式发现了搜索空间，而 K-均值的定位能力则在收敛时具有冲动性。这项研究对于高流行风险和低社会经济地位国家的流行病学疾病诊断很重要。
