POL Scientific / Bladder / Volume 7 / Issue 2 / DOI: 10.14440/bladder.2020.815
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RESEARCH ARTICLE

Classification of the urinary metabolome using machine learning and potential applications to diagnosing interstitial cystitis

Feng Tong1,  Muhammad Shahid2,  Peng Jin2,3,  Sungyong Jung4,  Won Hwa Kim1,  Jayoung Kim2,5,6,7*
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1 Department of Computer Science, University of Texas at Arlington, Arlington TX 76019, USA
2 Departments of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
3 Shengjing Hospital of China Medical University, Shenyang 110004, Liaoning, China
4 Department of Electrical Engineering, University of Texas, Arlington, Arlington, TX 76019, USA
5 Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
6 University of California Los Angeles, Los Angeles, CA 90095, USA
7 Department of Urology, Gachon University College of Medicine, Incheon 1 3 1 2 0, South Korea
Bladder 2020 , 7(2), 1–7;
Submitted: 20 March 2020 | Revised: 30 April 2020 | Accepted: 12 May 2020 | Published: 2 June 2020
© 2020 by the Author(s). Licensee POL Scientific, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

With the advent of artificial intelligence (AI) in biostatistical analysis and modeling, machine learning can potentially be applied into developing diagnostic models for interstitial cystitis (IC). In the current clinical setting, urologists are depen-dent on cystoscopy and questionnaire-based decisions to diagnose IC. This is a result of a lack of objective diagnostic molecular biomarkers. The purpose of this study was to develop a machine learning-based method for diagnosing IC and assess its performance using metabolomics profiles obtained from a prior study. To develop the machine learning algorithm, two classification methods, support vector machine (SVM) and logistic regression (LR), set at various pa-rameters, were applied to 43 IC patients and 16 healthy controls. There were 3 measures used in this study, accuracy, precision (positive predictive value), and recall (sensitivity). Individual precision and recall (PR) curves were drafted. Since the sample size was relatively small, complicated deep learning could not be done. We achieved a 76%–86% accuracy with leave-one-out cross validation depending on the method and parameters set. The highest accuracy achieved was 86.4% using SVM with a polynomial kernel degree set to 5, but a larger area under the curve (AUC) from the PR curve was achieved using LR with a l1-norm regularizer. The AUC was greater than 0.9 in its ability to discriminate IC patients from controls, suggesting that the algorithm works well in identifying IC, even when there is a class distribution imbal-ance between the IC and control samples. This finding provides further insight into utilizing previously identified urinary metabolic biomarkers in developing machine learning algorithms that can be applied in the clinical setting.

Keywords
interstitial cystitis
biomarker
urine
metabolomics
machine learning
artificial algorithm
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Bladder, Electronic ISSN: 2327-2120 Print ISSN: TBA, Published by POL Scientific
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