Appraisal of Clinical Explanatory Variables in Subtyping of Type 2 Diabetes Using Machine Learning Models
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Patients
2.3. Clustering Techniques
Validation of Explanatory Variables
2.4. Testing Similarity Between Clusters
2.4.1. The Adjusted Rand Index (ARI)
2.4.2. The Fowlkes–Mallows Index (FMI)
2.5. Statistics
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variables | Training Dataset (N = 348) | Prediction Dataset (N = 586) | p-Value |
---|---|---|---|
Age at diagnosis (years) | 41.92 (10.65) | 46.32 (9.27) | <0.001 |
Body mass index (kg/m2) | 31.27 (5.7) | 31.54 (6.04) | 0.247 |
Fasting blood glucose (mg/dL) | 146.86 (52.47) | 133 (44.16) | <0.001 |
HbA1c (%) | 7.63 (1.72) | 7 (1.33) | <0.001 |
Fasting serum insulin (µIU/nmol) | 14.04 (10.67) | 18.04 (10.74) | <0.001 |
Duration of type 2 diabetes (years) | 14.42 (8.14) | 3.67 (2.9) | <0.001 |
Supervised Cluster-Based Classification Using ML Predictive Models | |||||||
---|---|---|---|---|---|---|---|
SIRD | SIDD | MARD | MOD | MEOD | Total | ||
Direct unsupervised clustering | Scenario 1: FSI, FBG, and BMI | ||||||
SIRD | 46 | 2 | 28 | 5 | 0 | 81 | |
SIDD | 0 | 17 | 0 | 1 | 0 | 18 | |
MARD | 0 | 15 | 8 | 0 | 38 | 61 | |
MOD | 0 | 1 | 36 | 96 | 6 | 139 | |
MEOD | 0 | 0 | 78 | 0 | 209 | 287 | |
Total | 46 | 35 | 150 | 102 | 253 | 586 | |
Scenario 2: FSI, FBG, and BMI and age at diagnosis | |||||||
SIRD | 56 | 0 | 0 | 2 | 0 | 58 | |
SIDD | 3 | 28 | 1 | 1 | 0 | 33 | |
MARD | 1 | 3 | 185 | 0 | 21 | 210 | |
MOD | 20 | 0 | 66 | 39 | 0 | 125 | |
MEOD | 5 | 1 | 17 | 43 | 94 | 160 | |
Total | 85 | 32 | 269 | 85 | 115 | 586 | |
Scenario 4: FSI, FBG, and BMI, HbA1c and age at diagnosis | |||||||
SIRD | 62 | 0 | 2 | 0 | 0 | 64 | |
SIDD | 4 | 38 | 1 | 1 | 1 | 45 | |
MARD | 4 | 1 | 104 | 30 | 94 | 233 | |
MOD | 7 | 1 | 4 | 79 | 0 | 91 | |
MEOD | 0 | 2 | 90 | 9 | 52 | 153 | |
Total | 77 | 42 | 201 | 119 | 147 | 586 |
Supervised Cluster-Based Classification Using ML Predictive Models | |||||
---|---|---|---|---|---|
Direct unsupervised clustering | SIRD | SIDD | Mixed | Total | |
SIDD | 6 | 51 | 1 | 58 | |
SIRD | 136 | 1 | 81 | 218 | |
Mixed | 0 | 6 | 304 | 310 | |
Total | 142 | 58 | 386 | 586 |
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Khamis, A.H.; Abdul, F.; Dsouza, S.; Sulaiman, F.; Khyreim, C.; Siddig, M.E.; Bayoumi, R. Appraisal of Clinical Explanatory Variables in Subtyping of Type 2 Diabetes Using Machine Learning Models. J. Clin. Med. 2025, 14, 6548. https://doi.org/10.3390/jcm14186548
Khamis AH, Abdul F, Dsouza S, Sulaiman F, Khyreim C, Siddig ME, Bayoumi R. Appraisal of Clinical Explanatory Variables in Subtyping of Type 2 Diabetes Using Machine Learning Models. Journal of Clinical Medicine. 2025; 14(18):6548. https://doi.org/10.3390/jcm14186548
Chicago/Turabian StyleKhamis, Amar H., Fatima Abdul, Stafny Dsouza, Fatima Sulaiman, Costerwell Khyreim, Mohammed E. Siddig, and Riad Bayoumi. 2025. "Appraisal of Clinical Explanatory Variables in Subtyping of Type 2 Diabetes Using Machine Learning Models" Journal of Clinical Medicine 14, no. 18: 6548. https://doi.org/10.3390/jcm14186548
APA StyleKhamis, A. H., Abdul, F., Dsouza, S., Sulaiman, F., Khyreim, C., Siddig, M. E., & Bayoumi, R. (2025). Appraisal of Clinical Explanatory Variables in Subtyping of Type 2 Diabetes Using Machine Learning Models. Journal of Clinical Medicine, 14(18), 6548. https://doi.org/10.3390/jcm14186548