Key result
Machine learning numerically outperforms logistic regression for classifying DM and HTN comorbidity, reaching ~0.82 AUC.
Why the study?
The probability factors for diabetes comorbidity with hypertension among the elderly population and the comparative performance of machine learning versus traditional statistical methods were not well established.
Do machine learning models improve the identification of diabetes and hypertension comorbidity statuses compared to traditional logistic regression in Chinese older adults?
Cross-Sectional (n=4,704)
No
Do machine learning models improve the identification of diabetes and hypertension comorbidity statuses compared to traditional logistic regression in Chinese older adults?
Effect estimate: AUC 0.820 (95% CI 0.757-0.882)
Machine learning models can effectively identify risk factors for diabetes and hypertension comorbidity in older adults, achieving comparable or numerically better performance than traditional statistical methods.
To explore the probability factors for diabetes comorbidity with hypertension among the elderly population using machine learning (ML) methods and to compare the performance of ML models with traditional statistical methods. This cross-sectional study included 4,704 older adults who underwent health examinations between 2019 and 2021 at a tertiary hospital in Chengdu, Sichuan Province, China. Sociodemographic data, dietary habits, lifestyle factors, chronic disease diagnoses, and family history were assessed. Participants were divided into three groups: no diabetes (No DM), diabetes without hypertension (DM no HTN), and diabetes with hypertension (DM with HTN). We used four ML methods (random forest, LASSO, neural network, ridge regression) to build three classifier models: task 1 (DM no HTN vs. No DM), task 2 (DM with HTN vs. No DM), and task 3 (DM with HTN vs. DM no HTN). Traditional logistic regression models were also performed for comparison. Machine learning (ML) models achieved numerically higher AUC values compared to traditional logistic regression in classification tasks, though these differences did not reach statistical significance. Neural networks achieved the best performance, with an AUC of 0.790 (95% CI: 0.707–0.873), in distinguishing individuals with no diabetes mellitus (DM) from those with DM but no hypertension (HTN). LASSO regression demonstrated the best performance, with an AUC of 0.820 (95% CI: 0.757–0.882), in distinguishing individuals with no DM from those with DM and HTN. Random forests achieved the best performance, with an AUC of 0.783 (95% CI: 0.700-0.867), in distinguishing individuals with DM but no HTN from those with DM and HTN. Coronary heart disease, excessive intake of meat and fat, and age over 75 years were important features in task (1) Insufficient intake of vegetables and fruits, coronary heart disease, and sedentariness were important features in task (2) Excessive intake of meat and fat, family history of hypertension, and insufficient consumption of whole grains were important features in task 3. This study is the first to predict different statuses of hypertension comorbidity in Chinese elderly with diabetes using ML methods. Our findings provide valuable insights for preventing comorbidity in elderly population with diabetes.
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Wang et al. (2026) conducted a cross-sectional in Diabetes and hypertension comorbidity (n=4,704). Machine learning models vs. Traditional logistic regression was evaluated on Distinguishing individuals with no DM from those with DM and HTN (AUC 0.820, 95% CI 0.757-0.882). Machine learning models achieved numerically higher AUC values than logistic regression for classifying diabetes and hypertension comorbidity, with LASSO achieving an AUC of 0.820 (95% CI 0.757-0.882).
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