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Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study
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 endpoi...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267784/ https://www.ncbi.nlm.nih.gov/pubmed/35806944 http://dx.doi.org/10.3390/jcm11133661 |
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author | Huang, Li-Ying Chen, Fang-Yu Jhou, Mao-Jhen Kuo, Chun-Heng Wu, Chung-Ze Lu, Chieh-Hua Chen, Yen-Lin Pei, Dee Cheng, Yu-Fang Lu, Chi-Jie |
author_facet | Huang, Li-Ying Chen, Fang-Yu Jhou, Mao-Jhen Kuo, Chun-Heng Wu, Chung-Ze Lu, Chieh-Hua Chen, Yen-Lin Pei, Dee Cheng, Yu-Fang Lu, Chi-Jie |
author_sort | Huang, Li-Ying |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9267784 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92677842022-07-09 Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study Huang, Li-Ying Chen, Fang-Yu Jhou, Mao-Jhen Kuo, Chun-Heng Wu, Chung-Ze Lu, Chieh-Hua Chen, Yen-Lin Pei, Dee Cheng, Yu-Fang Lu, Chi-Jie J Clin Med Article 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. MDPI 2022-06-24 /pmc/articles/PMC9267784/ /pubmed/35806944 http://dx.doi.org/10.3390/jcm11133661 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Huang, Li-Ying Chen, Fang-Yu Jhou, Mao-Jhen Kuo, Chun-Heng Wu, Chung-Ze Lu, Chieh-Hua Chen, Yen-Lin Pei, Dee Cheng, Yu-Fang Lu, Chi-Jie Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study |
title | Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study |
title_full | Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study |
title_fullStr | Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study |
title_full_unstemmed | Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study |
title_short | Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin–Creatinine Ratio in a 4-Year Follow-Up Study |
title_sort | comparing multiple linear regression and machine learning in predicting diabetic urine albumin–creatinine ratio in a 4-year follow-up study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9267784/ https://www.ncbi.nlm.nih.gov/pubmed/35806944 http://dx.doi.org/10.3390/jcm11133661 |
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