Analysis of Diabetic Patients' Data for Clustering and Prescription Drug Based on Proposed Algorithm

Introduction:

 Diabetes is a metabolic disorder in the body that is impaired by the ability to produce insulin hormone. The main purpose of the present study is to discover the hidden knowledge in the data of diabetic patients, which can assist clinicians in clustering new patients and prescribing appropriate medication according to each cluster.

In this paper, we use MR-VDBSCAN algorithm. The implementation of this algorithm is based on the map-reduce framework of Hadoop. The main idea of the research is to use local density to find the density of each point. This strategy can prevent clusters from joining at different densities.

The algorithm is based on the selected dataset, tested and evaluated, and the results show high accuracy and efficiency. The results were compared with the results of k-Means clustering, The MR-VDBSCAN algorithm has a higher execution speed than that of the algorithm and has the ability to detect clusters with different density of superiority of this algorithm than the comparable algorithm. The results show that the MR-VDBSCAN algorithm can provide better performance than other algorithms. In particular, the similarity of the proposed algorithm is 97% for the diabetes set.

The results show that the MR-VDBSCAN algorithm performs better clustering than the K-means algorithm and can place patients into subgroups that assist physicians in prescribing.