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Clustering Heatmap for Visualizing and Exploring Complex and High-dimensional Data Related to Chronic Kidney Disease
Cheng-Sheng Yu1,2, Chang-Hsien Lin1,2, Yu-Jiun Lin1,2
1Department of Family Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan.
Journal of Clinical Medicine
|February 7, 2020
Summary
Identifying key risk factors like uric acid and HbA1c aids in early chronic kidney disease (CKD) detection. This helps manage high-risk patients and prevent rapid disease progression.
Area of Science:
- Nephrology
- Preventive Medicine
- Data Science in Healthcare
Background:
- Chronic kidney disease (CKD) often presents late, necessitating early risk factor identification.
- Preventive strategies are crucial for managing CKD and preventing adverse outcomes.
- Recognizing rapid CKD progression in high-risk groups is vital for timely intervention.
Purpose of the Study:
- To identify significant risk factors associated with chronic kidney disease (CKD).
- To develop a predictive model for identifying patients at high risk of rapid CKD progression.
- To utilize advanced data visualization techniques for CKD patient classification.
Main Methods:
- Retrospective cohort study design.
- Multivariate statistical analysis to assess CKD prediction.
- Clustering heatmap and random forest for interactive visualization and classification of CKD stages.
Main Results:
- Uric acid, blood urea nitrogen, waist circumference, serum glutamic oxaloacetic transaminase, and hemoglobin A1c (HbA1c) were significantly linked to CKD.
- CKD showed strong associations with obesity, hyperglycemia, and liver function.
- Heatmap analysis revealed clustering patterns for hypertension, HbA1c, and high-density lipoprotein cholesterol, indicating shared or opposing risk profiles.
Conclusions:
- A novel predictive model using clustering heatmaps can aid healthcare management for patients at risk of rapid CKD progression.
- This model assists physicians in accurate diagnosis and management of progressive CKD.
- Early identification of patients with similar risk profiles to advanced CKD stages is crucial for close monitoring.
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