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

Document Type : Research Paper

Authors

1 PhD student, Department of Information Technology Management, Islamic Azad University Science and Research Branch, Tehran, Iran

2 Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Department of Information Technology Managemen tScience and Research Branch, Islamic Azad University,Tehran,Iran

4 Associate Professor, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

5 Department of management, Tarbiat Modares University, Tehran, Iran

Abstract

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.
Methods: 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.
Results: 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.
Conclusion: 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.

Keywords

References

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Medical Journal of Mashhad university of Medical Sciences
Volume 63, Issue 2
July and August 2020
Pages 2358-2368

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