Usage of cluster algorithms in health studies: An application

With clustering methods variable and individuals which have similar characteristics may be collected in a group. Although clustering methods have many applications, there are limited studies in health researchs in our country. While the purpose of this study is to introduce different clustering algorithms and show how and which cases shoul be correctly used. At the same time, different clustering algorithms results which can be applied on a real data set were compared. According to the evaluations, for two different data sets the kappa coefficients were statististically significant and its degree are intermediate. In terms of both data sets the most convenient and fastest algorithm is farthest clustering algorithm. The results obtained by Make Density Based and EM algorithms gave the most accurate desicions in terms of the distribution of the groups among Framingham risk groups crosstables. As a result, with taking into account the criterion of clinical information it is thought that the examination of clustering of risk factors of the disease, will be played an inportant role for intorduction of accurate disease diagnosis. In addition we believe that when considering data distribution and characteristics of data sets clustering algorithms can be used as a diagnostic tool for the plannings and diagnosis of diseases in the field of health.