Enhancing Cluster Accuracy in Diabetes Multimorbidity With Dirichlet Process Mixture Models

Enhancing Cluster Accuracy in Diabetes Multimorbidity With Dirichlet Process Mixture Models

Clustering of diabetic multimorbidity data from EHRs is challenging due to patient heterogeneity, high-dimensional variables, sensitivity to parameter settings, and high computational demands, which complicate clustering processes and may result in suboptimal clustering results. These complex and im...

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Bibliographic Details
Main Authors: Francis John Kita, Srinivasa Rao Gaddes, Peter Josephat Kirigiti
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Clustering methods
diabetic multimorbidity
Dirichlet process mixture model
infinite mixture models
validation metrics
Online Access:https://ieeexplore.ieee.org/document/10816607/
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