A Real-Time Data Clustering Scheme Using K-Medoids Based Optimal Neural Network Approach for Integrating Demographics and Diagnosis Codes

Abstract

Data clustering from the Electronic Health Record (EHR) system is an important system for diagnosing the association between the preprocessing phase and the clinical profiles of patients. According to prior studies, data heterogeneity, high costs associated with medical equipment, privacy and utility preservation, computational complexity, and other aspects make developing new clustering algorithms difficult. In this study, we introduce K-Medoids-based Optimal Neural Network (Km-ONN) to cluster the real-time (RT) dataset. Both diagnosis codes and demographic attributes of patient’s values are present in an RT dataset. Based on diagnosis codes and demographic codes, the identity disclosure is prevented from attackers by applying (K, Kⁿ) in which the information loss is minimized. The data owner specifies the utility constraint that restricts the number of generalizations. In this analysis, we used two EHR datasets, INFORMS and VERMONT, on which the proposed model performed better. With a number of 1000 medoids, the proposed method offers an accuracy, recall, Mathews correlation coefficient(MCC), and F-score of 0.99, 0.998,0.999, and 1 during training. The proposed model, on the other hand, is well suited for clustering RT datasets, resulting in more scalable and efficient outputs than other existing models.

KEYWORDS:

• Demographic attributes
• Diagnosis codes
• K-medoids and Optimal Neural Network
• RT dataset

Additional information

Notes on contributors

S. Srijayanthi

S Srijayanthi received her BE degree in computer science and engineering from University of Madras and ME degree in computer science and engineering from Anna University Chennai, India, in 2002 and 2013, respectively. She is now pursuing the PhD degree in information and communication engineering at Anna University, Chennai. Her research interests include privacy and data mining.

T. Sethukarasi

T Sethukarasi received her PhD from Anna University Chennai, India in 2013. She is currently a professor and head in the Department of Computer Science and Engineering at R M K Engineering College. Her research interests include data mining, soft computing and network security. Email: [email protected]