{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T15:07:10Z","timestamp":1788275230763,"version":"build-2803163510"},"reference-count":28,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Office of the Assistant Secretary of Defense for Health Affairs","award":["W81XWH-19-1-0288"],"award-info":[{"award-number":["W81XWH-19-1-0288"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives. This study aimed to improve machine learning models for diagnosing interstitial cystitis\/bladder pain syndrome (IC\/BPS) by comparing classical machine learning methods with newer AutoML approaches, utilizing biomarker data and patient-reported outcomes as features. Methods. We applied various machine learning techniques to biomarker data from the previous IP4IC and ICRS studies to predict the presence of IC\/BPS, a disorder impacting the urinary bladder. Data were sourced from two nationwide, crowd-sourced collections of urine samples involving 2009 participants. The models utilized included logistic regression, support vector machines, random forests, k-nearest neighbors, and AutoGluon. Results. Expanding the dataset for model training and evaluation resulted in improved performance metrics compared to previously published findings. The implementation of AutoML methods yielded enhancements in model accuracy over classical techniques. The top-performing models achieved a receiver-operating characteristic area under the curve (ROC-AUC) of up to 0.96. Conclusions. This research demonstrates an improvement in model performance relative to earlier studies, with the top model for binary classification incorporating objective urinary biomarker levels. These advancements represent a significant step toward developing a reliable classification model for the diagnosis of IC\/BPS.<\/jats:p>","DOI":"10.3390\/diagnostics14232734","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T04:13:03Z","timestamp":1733371983000},"page":"2734","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Advancing Interstitial Cystitis\/Bladder Pain Syndrome (IC\/BPS) Diagnosis: A Comparative Analysis of Machine Learning Methodologies"],"prefix":"10.3390","volume":"14","author":[{"given":"Joseph J.","family":"Janicki","sequence":"first","affiliation":[{"name":"Underactive Bladder Foundation, Pittsburgh, PA 15235, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bernadette M. M.","family":"Zwaans","sequence":"additional","affiliation":[{"name":"Corewell Health William Beaumont University Hospital, Royal Oak, MI 48073, USA"},{"name":"Department of Urology, Oakland University William Beaumont School of Medicine, Rochester, MI 48309, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah N.","family":"Bartolone","sequence":"additional","affiliation":[{"name":"Corewell Health William Beaumont University Hospital, Royal Oak, MI 48073, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elijah P.","family":"Ward","sequence":"additional","affiliation":[{"name":"Corewell Health William Beaumont University Hospital, Royal Oak, MI 48073, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9480-8972","authenticated-orcid":false,"given":"Michael B.","family":"Chancellor","sequence":"additional","affiliation":[{"name":"Underactive Bladder Foundation, Pittsburgh, PA 15235, USA"},{"name":"Department of Urology, Oakland University William Beaumont School of Medicine, Rochester, MI 48309, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1002\/nau.22559","article-title":"An overactive bladder symptom and health-related quality of life short-form: Validation of the OAB-q S.F","volume":"34","author":"Coyne","year":"2015","journal-title":"Neurourol. Urodyn."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1097\/JU.0000000000002756","article-title":"Diagnosis and treatment of interstitial cystitis\/bladder pain syndrome","volume":"208","author":"Clemens","year":"2022","journal-title":"J. Urol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1016\/j.juro.2015.01.086","article-title":"Diagnosis and treatment of interstitial cystitis\/bladder pain syndrome: AUA guideline amendment","volume":"193","author":"Hanno","year":"2015","journal-title":"J. Urol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.juro.2012.08.088","article-title":"The prevalence and overlap of interstitial cystitis\/bladder pain syndrome and chronic prostatitis\/chronic pelvic pain syndrome in men: Results of the RAND Interstitial Cystitis Epidemiology male study","volume":"189","author":"Suskind","year":"2013","journal-title":"J. Urol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/luts.12344","article-title":"Clinical characterization of interstitial cystitis\/bladder pain syndrome in women based on the presence or absence of Hunner lesions and glomerulations","volume":"13","author":"Watanabe","year":"2021","journal-title":"Low Urin. Tract Symptoms."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.juro.2011.03.132","article-title":"Prevalence of symptoms of bladder pain syndrome\/interstitial cystitis among adult females in the United States","volume":"186","author":"Berry","year":"2011","journal-title":"J. Urol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1097\/JU.0000000000001031","article-title":"Hunner lesion phenotype in interstitial cystitis\/bladder pain syndrome: A systematic review and meta-analysis","volume":"204","author":"Lai","year":"2020","journal-title":"J. Urol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Clemens, J.Q., Mullins, C., Kusek, J.W., Kirkali, Z., Mayer, E.A., Rodr\u00edguez, L.V., Klumpp, D.J., Schaeffer, A.J., Kreder, K.J., and Buchwald, D. (2014). The MAPP research network: A novel study of urologic chronic pelvic pain syndromes. BMC Urol., 14.","DOI":"10.1186\/1471-2490-14-57"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"478","DOI":"10.5489\/cuaj.1179","article-title":"Hunner\u2019s lesions","volume":"3","author":"Teichman","year":"2009","journal-title":"Can. Urol. Assoc. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1111\/iju.13971","article-title":"Hunner lesion versus non-Hunner lesion interstitial cystitis\/bladder pain syndrome","volume":"26","author":"Whitmore","year":"2019","journal-title":"Int. J. Urol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1097\/JU.0000000000000257","article-title":"Biomarkers implicated in lower urinary tract symptoms: Systematic review and pathway analyses","volume":"202","author":"Siddiqui","year":"2019","journal-title":"J. Urol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1007\/s00192-017-3547-5","article-title":"Comparison of inflammatory urine markers in patients with interstitial cystitis and overactive bladder","volume":"29","author":"Furuta","year":"2018","journal-title":"Int. Urogynecol. J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jiang, Y.H., Jhang, J.F., and Kuo, H.C. (2022). Can we use urinary cytokine\/chemokine analysis in discriminating ulcer-type interstitial cystitis\/bladder pain syndrome?. Diagnostics, 12.","DOI":"10.3390\/diagnostics12051093"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jiang, Y.-H., Jhang, J.-F., Hsu, Y.-H., Ho, H.-C., Wu, Y.-H., and Kuo, H.-C. (2021). Urine biomarkers in ESSIC type 2 interstitial cystitis\/bladder pain syndrome and overactive bladder with developing a novel diagnostic algorithm. Sci. Rep., 11.","DOI":"10.1038\/s41598-020-80131-5"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1007\/s11255-009-9647-5","article-title":"Urine cytokines suggest an inflammatory response in the overactive bladder: A pilot study","volume":"42","author":"Tyagi","year":"2010","journal-title":"Int. Urol. Nephrol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"F1391","DOI":"10.1152\/ajprenal.00051.2020","article-title":"Urine cytokines as biomarkers for diagnosing interstitial cystitis\/bladder pain syndrome and mapping its clinical characteristics","volume":"318","author":"Jiang","year":"2020","journal-title":"J. Physiol. Renal. Physiol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kumari, N., Agrawal, U., Mishra, A.K., Kumar, A., Vasudeva, P., Mohanty, N.K., and Saxena, S. (2017). Predictive role of serum and urinary cytokines in invasion and recurrence of bladder cancer. Tumour. Biol. J. Int. Soc. Oncodev. Biol. Med., 39.","DOI":"10.1177\/1010428317697552"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1097\/01.ju.0000090192.36436.d5","article-title":"Monocyte chemoattractant protein-1 production by human detrusor smooth muscle cells","volume":"171","author":"Bouchelouche","year":"2004","journal-title":"J. Urol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1080\/02813430902757634","article-title":"Cytokines in urine in elderly subjects with acute cystitis and asymptomatic bacteriuria","volume":"27","author":"Rodhe","year":"2009","journal-title":"Scand. J. Prim. Health Care"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.urology.2024.03.043","article-title":"Risk Classification for Interstitial Cystitis\/Bladder Pain Syndrome Using Machine Learning Based Predictions","volume":"189","author":"Lamb","year":"2024","journal-title":"Urology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2","DOI":"10.4103\/UROS.UROS_155_20","article-title":"Toward a validated diagnostic test with machine learning algorithm for interstitial cystitis","volume":"32","author":"Chancellor","year":"2021","journal-title":"Urol. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lamb, L.E., Janicki, J.J., Bartolone, S.N., Peters, K.M., and Chancellor, M.B. (2017). Development of an interstitial cystitis risk score for bladder permeability. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0185686"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"20552076231216280","DOI":"10.1177\/20552076231216280","article-title":"Comparing online crowdsourcing with clinic patient enrollment: Findings from the IP4IC Study on interstitial cystitis\/bladder pain syndrome","volume":"9","author":"Janicki","year":"2023","journal-title":"Digit Health"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1016\/j.juro.2017.09.167","article-title":"Crowdsourcing disease biomarker discovery research: The IP4IC study","volume":"199","author":"Chancellor","year":"2018","journal-title":"J. Urol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/S0090-4295(99)80333-1","article-title":"The interstitial cystitis symptom index and problem index","volume":"49","author":"Sant","year":"1997","journal-title":"Urology"},{"key":"ref_26","first-page":"594","article-title":"Test-retest reliability and discriminant validity for the Brazilian version of \u201cThe Interstitial Cystitis Symptom Index and Problem Index\u201d and \u201cPelvic Pain and Urgency\/Frequency (PUF) Patient Symptom Scale\u201d instruments","volume":"4","author":"Victal","year":"2015","journal-title":"Transl. Androl. Urol."},{"key":"ref_27","unstructured":"Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., and Smola, A. (2020). AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. arXiv."},{"key":"ref_28","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2734\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:47:20Z","timestamp":1760114840000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2734"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,5]]},"references-count":28,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["diagnostics14232734"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14232734","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,5]]}}}