Exploration of Machine Learning for Hyperuricemia Prediction Models Based on Basic Health Checkup Tests.

Sangwoo Lee,Boram Park,Eun Choe

doi:10.3390/jcm8020172

Abstract

Background: Machine learning (ML) is a promising methodology for classification and prediction applications in healthcare. However, this method has not been practically established for clinical data. Hyperuricemia is a biomarker of various chronic diseases. We aimed to predict uric acid status from basic healthcare checkup test results using several ML algorithms and to evaluate the performance. Methods: We designed a prediction model for hyperuricemia using a comprehensive health checkup database designed by the classification of ML algorithms, such as discrimination analysis, K-nearest neighbor, naïve Bayes (NBC), support vector machine, decision tree, and random forest classification (RFC). The performance of each algorithm was evaluated and compared with the performance of a conventional logistic regression (CLR) algorithm by receiver operating characteristic curve analysis. Results: Of the 38,001 participants, 7705 were hyperuricemic. For the maximum sensitivity criterion, NBC showed the highest sensitivity (0.73), and RFC showed the second highest (0.66); for the maximum balanced classification rate (BCR) criterion, RFC showed the highest BCR (0.68), and NBC showed the second highest (0.66) among the various ML algorithms for predicting uric acid status. In a comparison to the performance of NBC (area under the curve (AUC) = 0.669, 95% confidence intervals (CI) = 0.669–0.675) and RFC (AUC = 0.775, 95% CI 0.770–0.780) with a CLR algorithm (AUC = 0.568, 95% CI = 0.563–0.571), NBC and RFC showed significantly better performance (p < 0.001). Conclusions: The ML model was superior to the CLR model for the prediction of hyperuricemia. Future studies are needed to determine the best-performing ML algorithms based on data set characteristics. We believe that this study will be informative for studies using ML tools in clinical research.

Highlights

Machine learning (ML) is a promising methodology for classification and prediction applications in healthcare
We evaluated the performance of the developed models and compared the best-performing algorithms of each ML model with conventional logistic regression (CLR) by measuring the area under the receiver operating characteristic (ROC) curves (AUC) on the combined set, which includes the training and test sets
We evaluated the most common ML models, namely, discriminant analysis classification (DAC) [9], decision tree classification (DTC) [10], K-nearest neighbor classification (KNNC) [11,12], naïve Bayes classification (NBC) [13,14], random forest classification (RFC) [15,16], and support vector machine classification (SVMC) [17,18]

Summary

Introduction

Machine learning (ML) is a promising methodology for classification and prediction applications in healthcare. This method has not been practically established for clinical data. We aimed to predict uric acid status from basic healthcare checkup test results using several ML algorithms and to evaluate the performance. Methods: We designed a prediction model for hyperuricemia using a comprehensive health checkup database designed by the classification of ML algorithms, such as discrimination analysis, K-nearest neighbor, naïve Bayes (NBC), support vector machine, decision tree, and random forest classification (RFC). Most studies using clinical data to date have been analyzed with conventional statistical models. ML can consider all possible interactions between various input data [3]

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Conclusion