Development and Validation of an Insulin Resistance Model for a Population with Chronic Kidney Disease Using a Machine Learning Approach

Abstract

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.

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

“…Existing studies that utilize ML methods to perform CKD data analysis mostly analyze the patients directly [ 16 , 17 , 20 , 31 , 32 , 33 , 34 ], and few studies have discussed the predictive models and important risk factors for CKD patients with MetS. Several studies have constructed predictive models for MetS patients, as well as their risk factors.…”

Simple Method to Predict Insulin Resistance in Children Aged 6–12 Years by Using Machine Learning

“…Park et al and Lee et al both explored the prediction of IR; however, the main populations of these existing investigations were adults or patients with chronic kidney disease, in contrast to the pediatric patients we desired. 18 , 19 In addition, many features were included in these studies, especially some biochemical blood tests, which, on the one hand, are not often performed during routine physical examinations in primary care hospitals as they may increase the expenses of families, and, on the other hand, require fast blood samples, which may be intimidating or aversive for many children. These reasons may hinder the diffusion of these models.…”

Machine Learning Soft Voting Algorithm for Prediction and Detection of Nonalcoholic Fatty Liver Disease

“…Machine learning is a eld of arti cial intelligence study that applies statistical approaches to classifying data. Several machine learning techniques have been applied in clinical settings to predict diseases and have shown higher accuracy for diagnosis than classical methods [9][10][11][12] . Here, we propose a new machine learning Voting algorithm to study useful predictive model for NAFLD.…”