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

Journal of Clinical Medicine

Boram Park¹  EunKyung Choe²  Sangwoo Lee³ 

[1] Department of Biomedical Science, Seoul National University Graduate School, Seoul 03081, Korea;Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, Seoul 06236, Korea;Network Division, Samsung Electronics, Suwon 16677, Korea;

关键词: machine learning;    prediction;    uric acid;   

DOI  :  10.3390/jcm8020172

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown