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J. Clin. Med. 2019, 8(2), 172;

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

Network Division, Samsung Electronics, Suwon 16677, Korea
Department of Surgery, Seoul National University Hospital Healthcare System Gangnam Center, Seoul 06236, Korea
Department of Surgery, Seoul National University College of Medicine, Seoul 03080, Korea
Department of Biomedical Science, Seoul National University Graduate School, Seoul 03081, Korea
Author to whom correspondence should be addressed.
Received: 4 January 2019 / Revised: 31 January 2019 / Accepted: 31 January 2019 / Published: 2 February 2019
(This article belongs to the Special Issue The Future of Artificial Intelligence in Clinical Medicine)
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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. View Full-Text
Keywords: machine learning; prediction; uric acid machine learning; prediction; uric acid
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Lee, S.; Choe, E.K.; Park, B. Exploration of Machine Learning for Hyperuricemia Prediction Models Based on Basic Health Checkup Tests. J. Clin. Med. 2019, 8, 172.

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