Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach
Background: Chronic kidney disease (CKD) is a complex syndrome without a definitive treatment. For these patients, insulin resistance (IR) is associated with worse renal and patient outcomes. Until now, no predictive model using machine learning (ML) has been reported on IR in CKD patients. Methods:...
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MDPI AG
2022-07-01
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| Series: | Nutrients |
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| Online Access: | https://www.mdpi.com/2072-6643/14/14/2832 |
| _version_ | 1797416598987866112 |
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| author | Chia-Lin Lee Wei-Ju Liu Shang-Feng Tsai |
| author_facet | Chia-Lin Lee Wei-Ju Liu Shang-Feng Tsai |
| author_sort | Chia-Lin Lee |
| collection | DOAJ |
| description | Background: Chronic kidney disease (CKD) is a complex syndrome without a definitive treatment. For these patients, insulin resistance (IR) is associated with worse renal and patient outcomes. Until now, no predictive model using machine learning (ML) has been reported on IR in CKD patients. Methods: The CKD population studied was based on results from the National Health and Nutrition Examination Survey (NHANES) of the USA from 1999 to 2012. The homeostasis model assessment of IR (HOMA-IR) was used to assess insulin resistance. We began the model building process via the ML algorithm (random forest (RF), eXtreme Gradient Boosting (XGboost), logistic regression algorithms, and deep neural learning (DNN)). We compared different receiver operating characteristic (ROC) curves from different algorithms. Finally, we used SHAP values (SHapley Additive exPlanations) to explain how the different ML models worked. Results: In this study population, 71,916 participants were enrolled. Finally, we analyzed 1,229 of these participants. Their data were segregated into the IR group (HOMA IR > 3, n = 572) or non-IR group (HOMR IR ≤ 3, n = 657). In the validation group, RF had a higher accuracy (0.77), specificity (0.81), PPV (0.77), and NPV (0.77). In the test group, XGboost had a higher AUC of ROC (0.78). In addition, XGBoost also had a higher accuracy (0.7) and NPV (0.71). RF had a higher accuracy (0.7), specificity (0.78), and PPV (0.7). In the RF algorithm, the body mass index had a much larger impact on IR (0.1654), followed by triglyceride (0.0117), the daily calorie intake (0.0602), blood HDL value (0.0587), and age (0.0446). As for the SHAP value, in the RF algorithm, almost all features were well separated to show a positive or negative association with IR. Conclusion: This was the first study using ML to predict IR in patients with CKD. Our results showed that the RF algorithm had the best AUC of ROC and the best SHAP value differentiation. This was also the first study that included both macronutrients and micronutrients. We concluded that ML algorithms, particularly RF, can help determine risk factors and predict IR in patients with CKD. |
| first_indexed | 2024-03-09T06:05:34Z |
| format | Article |
| id | doaj.art-93415054b48b43869bcd78a3e0055a97 |
| institution | Directory Open Access Journal |
| issn | 2072-6643 |
| language | English |
| last_indexed | 2024-03-09T06:05:34Z |
| publishDate | 2022-07-01 |
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| series | Nutrients |
| spelling | doaj.art-93415054b48b43869bcd78a3e0055a972023-12-03T12:04:00ZengMDPI AGNutrients2072-66432022-07-011414283210.3390/nu14142832Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning ApproachChia-Lin Lee0Wei-Ju Liu1Shang-Feng Tsai2Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 407219, TaiwanDepartment of Medical Research, Taichung Veterans General Hospital, Taichung 407219, TaiwanSchool of Medicine, National Yang-Ming University, Taipei 112304, TaiwanBackground: Chronic kidney disease (CKD) is a complex syndrome without a definitive treatment. For these patients, insulin resistance (IR) is associated with worse renal and patient outcomes. Until now, no predictive model using machine learning (ML) has been reported on IR in CKD patients. Methods: The CKD population studied was based on results from the National Health and Nutrition Examination Survey (NHANES) of the USA from 1999 to 2012. The homeostasis model assessment of IR (HOMA-IR) was used to assess insulin resistance. We began the model building process via the ML algorithm (random forest (RF), eXtreme Gradient Boosting (XGboost), logistic regression algorithms, and deep neural learning (DNN)). We compared different receiver operating characteristic (ROC) curves from different algorithms. Finally, we used SHAP values (SHapley Additive exPlanations) to explain how the different ML models worked. Results: In this study population, 71,916 participants were enrolled. Finally, we analyzed 1,229 of these participants. Their data were segregated into the IR group (HOMA IR > 3, n = 572) or non-IR group (HOMR IR ≤ 3, n = 657). In the validation group, RF had a higher accuracy (0.77), specificity (0.81), PPV (0.77), and NPV (0.77). In the test group, XGboost had a higher AUC of ROC (0.78). In addition, XGBoost also had a higher accuracy (0.7) and NPV (0.71). RF had a higher accuracy (0.7), specificity (0.78), and PPV (0.7). In the RF algorithm, the body mass index had a much larger impact on IR (0.1654), followed by triglyceride (0.0117), the daily calorie intake (0.0602), blood HDL value (0.0587), and age (0.0446). As for the SHAP value, in the RF algorithm, almost all features were well separated to show a positive or negative association with IR. Conclusion: This was the first study using ML to predict IR in patients with CKD. Our results showed that the RF algorithm had the best AUC of ROC and the best SHAP value differentiation. This was also the first study that included both macronutrients and micronutrients. We concluded that ML algorithms, particularly RF, can help determine risk factors and predict IR in patients with CKD.https://www.mdpi.com/2072-6643/14/14/2832insulin resistanceHOMA-IRchronic kidney diseaseartificial intelligencemachine learningdeep learning |
| spellingShingle | Chia-Lin Lee Wei-Ju Liu Shang-Feng Tsai Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach Nutrients insulin resistance HOMA-IR chronic kidney disease artificial intelligence machine learning deep learning |
| title | Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach |
| title_full | Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach |
| title_fullStr | Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach |
| title_full_unstemmed | Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach |
| title_short | Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach |
| title_sort | development and validation of an insulin resistance model for a population with chronic kidney disease using a machine learning approach |
| topic | insulin resistance HOMA-IR chronic kidney disease artificial intelligence machine learning deep learning |
| url | https://www.mdpi.com/2072-6643/14/14/2832 |
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