ArticleJournal of clinical medicine2022
Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin-Creatinine Ratio in a 4-Year Follow-Up Study.
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Explainable prediction of MDR/RR-TB in tuberculosis-diabetes mellitus multimorbidity: a machine learning model developed and validated in a dual-center study.BMC infectious diseases · 2026Article
- The impact of knowledge of hospitalization on mortality predictions.Health services & outcomes research methodology · 2025Article
- Predicting and interpreting key features of refractory Mycoplasma pneumoniae pneumonia using multiple machine learning methods.Scientific reports · 2025Article
- Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets.Scientific reports · 2025Article
- Machine learning predictive model for aspiration risk in early enteral nutrition patients with severe acute pancreatitis.Heliyon · 2024Article
- Risk of Microvascular Complications in Newly Diagnosed Type 2 Diabetes Patients Using Automated Machine Learning Prediction Models.Journal of clinical medicine · 2024Article
- Challenges in predictive modelling of chronic kidney disease: A narrative review.World journal of nephrology · 2024Review
- Comparison of multiple linear regression and machine learning methods in predicting cognitive function in older Chinese type 2 diabetes patients.BMC neurology · 2024Article
- Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health Screening Indicators.Journal of clinical medicine · 2023Article
- An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages.Frontiers in medicine · 2023Article
- A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques.Healthcare (Basel, Switzerland) · 2022Article
- Clinical Research on Type 2 Diabetes: A Promising and Multifaceted Landscape.Journal of clinical medicine · 2022Article
- An Integrated Machine Learning Scheme for Predicting Mammographic Anomalies in High-Risk Individuals Using Questionnaire-Based Predictors.International journal of environmental research and public health · 2022Article
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10 authors.
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Abstract
The urine albumin-creatinine ratio (uACR) is a warning for the deterioration of renal function in type 2 diabetes (T2D). The early detection of ACR has become an important issue. Multiple linear regression (MLR) has traditionally been used to explore the relationships between risk factors and endpoints. Recently, machine learning (ML) methods have been widely applied in medicine. In the present study, four ML methods were used to predict the uACR in a T2D cohort. We hypothesized that (1) ML outperforms traditional MLR and (2) different ranks of the importance of the risk factors will be obtained. A total of 1147 patients with T2D were followed up for four years. MLR, classification and regression tree, random forest, stochastic gradient boosting, and eXtreme gradient boosting methods were used. Our findings show that the prediction errors of the ML methods are smaller than those of MLR, which indicates that ML is more accurate. The first six most important factors were baseline creatinine level, systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose. In conclusion, ML might be more accurate in predicting uACR in a T2D cohort than the traditional MLR, and the baseline creatinine level is the most important predictor, which is followed by systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose in Chinese patients with T2D.
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