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Updated: Jul 4, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Classifying self-management clusters of patients with mild cognitive impairment associated with diabetes: A
Yun-Xian Wang1,2, Yuan-Jiao Yan3, Rong Lin1
1The School of Nursing, Fujian Medical University, Fuzhou, China.
Aims And Objectives:
This study aims to propose a self-management clusters classification method to determine the self-management ability of elderly patients with mild cognitive impairment (MCI) associated with diabetes mellitus (DM).
Background:
MCI associated with DM is a common chronic disease in old adults. Self-management affects the disease progression of patients to a large extent. However, the comorbidity and patients' self-management ability are heterogeneous.
Design:
A cross-sectional study based on cluster analysis is designed in this paper.
Method:
The study included 235 participants. The diabetes self-management scale is used to evaluate the self-management ability of patients. SPSS 21.0 was used to analyse the data, including descriptive statistics, agglomerative hierarchical clustering with Ward's method before k-means clustering, k-means clustering analysis, analysis of variance and chi-square test.
Results:
Three clusters of self-management styles were classified as follows: Disease neglect type, life oriented type and medical dependence type. Among all participants, the percentages of the three clusters above are 9.78%, 32.77% and 57.45%, respectively. The difference between the six dimensions of each cluster is statistically significant.
Conclusion(S):
This study classified three groups of self-management styles, and each group has its own self-management characteristics. The characteristics of the three clusters may help to provide personalized self-management strategies and delay the disease progression of MCI associated with DM patients.
Relevance To Clinical Practice:
Typological methods can be used to discover the characteristics of patient clusters and provide personalized care to improve the efficiency of patient self-management to delay the progress of the disease.
Patient Or Public Contribution:
In our study, we invited patients and members of the public to participate in the research survey and conducted data collection.
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