Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms
ObjectiveMicroalbuminuria (MAU) occurs due to universal endothelial damage, which is strongly associated with kidney disease, stroke, myocardial infarction, and coronary artery disease. Screening patients at high risk for MAU may aid in the early identification of individuals with an increased risk...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-02-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2022.775275/full |
| _version_ | 1798024312454643712 |
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| author | Wei Lin Songchang Shi Huibin Huang Nengying Wang Junping Wen Gang Chen |
| author_facet | Wei Lin Songchang Shi Huibin Huang Nengying Wang Junping Wen Gang Chen |
| author_sort | Wei Lin |
| collection | DOAJ |
| description | ObjectiveMicroalbuminuria (MAU) occurs due to universal endothelial damage, which is strongly associated with kidney disease, stroke, myocardial infarction, and coronary artery disease. Screening patients at high risk for MAU may aid in the early identification of individuals with an increased risk of cardiovascular events and mortality. Hence, the present study aimed to establish a risk model for MAU by applying machine learning algorithms.MethodsThis cross-sectional study included 3,294 participants ranging in age from 16 to 93 years. R software was used to analyze missing values and to perform multiple imputation. The observed population was divided into a training set and a validation set according to a ratio of 7:3. The first risk model was constructed using the prepared data, following which variables with P <0.1 were extracted to build the second risk model. The second-stage model was then analyzed using a chi-square test, in which a P ≥ 0.05 was considered to indicate no difference in the fit of the models. Variables with P <0.05 in the second-stage model were considered important features related to the prevalence of MAU. A confusion matrix and calibration curve were used to evaluate the validity and reliability of the model. A series of risk prediction scores were established based on machine learning algorithms.ResultsSystolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglyceride (TG) levels, sex, age, and smoking were identified as predictors of MAU prevalence. Verification using a chi-square test, confusion matrix, and calibration curve indicated that the risk of MAU could be predicted based on the risk score.ConclusionBased on the ability of our machine learning algorithm to establish an effective risk score, we propose that comprehensive assessments of SBP, DBP, FBG, TG, gender, age, and smoking should be included in the screening process for MAU. |
| first_indexed | 2024-04-11T18:00:23Z |
| format | Article |
| id | doaj.art-fa705f38ecc34e70a1417feb8737b446 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-04-11T18:00:23Z |
| publishDate | 2022-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-fa705f38ecc34e70a1417feb8737b4462022-12-22T04:10:33ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2022-02-01910.3389/fmed.2022.775275775275Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning AlgorithmsWei Lin0Songchang Shi1Huibin Huang2Nengying Wang3Junping Wen4Gang Chen5Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, ChinaDepartment of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital South Brance, Fujian Provincial Hospital Jinshan Branch, Fuzhou, ChinaDepartment of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, ChinaDepartment of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, ChinaDepartment of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, ChinaDepartment of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, ChinaObjectiveMicroalbuminuria (MAU) occurs due to universal endothelial damage, which is strongly associated with kidney disease, stroke, myocardial infarction, and coronary artery disease. Screening patients at high risk for MAU may aid in the early identification of individuals with an increased risk of cardiovascular events and mortality. Hence, the present study aimed to establish a risk model for MAU by applying machine learning algorithms.MethodsThis cross-sectional study included 3,294 participants ranging in age from 16 to 93 years. R software was used to analyze missing values and to perform multiple imputation. The observed population was divided into a training set and a validation set according to a ratio of 7:3. The first risk model was constructed using the prepared data, following which variables with P <0.1 were extracted to build the second risk model. The second-stage model was then analyzed using a chi-square test, in which a P ≥ 0.05 was considered to indicate no difference in the fit of the models. Variables with P <0.05 in the second-stage model were considered important features related to the prevalence of MAU. A confusion matrix and calibration curve were used to evaluate the validity and reliability of the model. A series of risk prediction scores were established based on machine learning algorithms.ResultsSystolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglyceride (TG) levels, sex, age, and smoking were identified as predictors of MAU prevalence. Verification using a chi-square test, confusion matrix, and calibration curve indicated that the risk of MAU could be predicted based on the risk score.ConclusionBased on the ability of our machine learning algorithm to establish an effective risk score, we propose that comprehensive assessments of SBP, DBP, FBG, TG, gender, age, and smoking should be included in the screening process for MAU.https://www.frontiersin.org/articles/10.3389/fmed.2022.775275/fullmicroalbuminuriarisk scorerisk factorsmachine learningpredicting |
| spellingShingle | Wei Lin Songchang Shi Huibin Huang Nengying Wang Junping Wen Gang Chen Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms Frontiers in Medicine microalbuminuria risk score risk factors machine learning predicting |
| title | Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms |
| title_full | Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms |
| title_fullStr | Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms |
| title_full_unstemmed | Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms |
| title_short | Development of a Risk Model for Predicting Microalbuminuria in the Chinese Population Using Machine Learning Algorithms |
| title_sort | development of a risk model for predicting microalbuminuria in the chinese population using machine learning algorithms |
| topic | microalbuminuria risk score risk factors machine learning predicting |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2022.775275/full |
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