Diagnosing diabetes mellitus using k-means clustering method with robust centroids initialization based swarm intelligence algorithm

Blood sugar levels rise as a result of impaired glucose homeostasis, which is a feature of Diabetes Mellitus (DM). Coronary artery disease, nerve damage, diabetic retinal disease, kidney failure, and sex disorders are just a few of the consequences that can result from DM. DM has to be diagnosed early to minimize its effects and those of its consequences. One technique for automatically detecting DM based on the prior data is the clustering algorithm. However, the K-mean Clustering method is easy to be implemented, has fast computational time and adapted easily. However, the centroids of K-means are initialized randomly that causes to be stuck in local optima. For this reason, the robust centroids initialization on the K-means clustering method are necessary to handle this problem. This paper uses swarm intelligence algorithm for this goal since it is able to obtain solution near the global optima, is robust and easy to be implemented. As a result, this study proposes an improved K-means clustering-based technique for diagnosing DM. The K-means is improved by using robust centroid initialization-based swarm intelligence algorithm. The swarm intelligence algorithm which is used is Particle Swarm Optimization. By using experiments, the proposed method is confirmed better than the original K-means clustering in diagnosing DM.