Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study

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.

The comparison between multiple linear regression and machine learning methods in predicting cognitive function in Chinese type 2 diabetes

“…The methods of the present study were published in our previous study as following [19]. After fasting for 10 hours, blood samples were drawn for biochemical analyses.…”

“…As aforementioned, RF, SGB, NB and XGBoost were used in the present study to construct predictive models for predicting the score of CFA and to identify the rank of importance of these risk factors. These Mach-L methods have been applied in various healthcare applications and do not need prior assumptions regarding data distribution [19][20][21][22][23][24][25][26][27][28]. MLR was used as the benchmark for comparison.…”

A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques

“…ML techniques are extensively applied in numerous studies on medical informatics and suboptimal health status [ 3 , 16 , 21 , 22 , 23 , 24 ]. They are often used to identify critical predictor variables or risk factors as they can effectively investigate the complex relationships between risk factors and outcomes, based on their promising predictive performance with vast amounts of medical data [ 16 , 22 , 23 , 25 ].…”

An Integrated Machine Learning Scheme for Predicting Mammographic Anomalies in High-Risk Individuals Using Questionnaire-Based Predictors

“…Machine learning (ML) is a branch of artificial intelligence that develops and utilizes algorithms for the data to mimic the way that humans learn [ 11 , 12 ]. ML techniques have been widely and successfully used in healthcare and medical research [ 11 , 12 , 13 , 14 , 15 ]. However, to the best knowledge of the authors, no studies have investigated the important risk factors related to predicting positive mammographic findings based on questionnaire-based demographic and obstetric/gynecological parameters using ML algorithms.…”