Development and Validation of a Machine Learning Algorithm to Classify Lower Urinary Tract Symptoms

Objective Lower urinary tract symptoms (LUTS), such as urinary urgency, frequency, and incontinence, affect the majority of the population, causing substantial morbidity, yet few receive effective care. Sizeable symptomatic overlap between LUTS categories leads to high rates of misdiagnosis. To improve diagnostic accuracy, we sought to employ machine learning approaches to LUTS categorization to generate diagnostic groupings based on patient-reported clinical data, creating a novel tool for diagnosis of patients with voiding complaints. Methods Questionnaire responses in a Development Dataset of 514 female subjects were used for model development, identifying 4 major clusters and 9 specific phenotypes of LUTS using agglomerative hierarchical clustering. Each cluster and phenotype was assigned a clinical identity consistent with recognized causes of voiding dysfunction by the consensus of two urologic specialists. Then, a random forest classifier was trained to assign unseen patients into these phenotypes. That model was then applied to a Validation Dataset of 571 additional subjects to confirm the diagnostic algorithm. Results This data-driven, hierarchical clustering approach captured overlapping symptoms inherent in typical patients, recognizing common uncomplicated diagnoses (i.e., overactive bladder) as well as several underrecognized diagnostic categories (i.e., myofascial pelvic pain). A diagnostic algorithm derived by supervised machine learning to assign unseen subjects into these phenotypes demonstrated good reproducibillty of the phenotypes and their symptomatic patterns in an independent Validation Dataset. Conclusions We describe the generation of a machine learning algorithm relying only on validated, patient-reported symptoms for diagnostic classification. Given a growing physician shortage and increasing challenges for patients accessing specialist care, this type of digital technology holds great potential to improve the recognition and diagnosis of functional urologic conditions. ### Competing Interest Statement A.L. Ackerman receives grant funding from Medtronic, Inc. and MicrogenDx, is an advisor for Abbvie and Watershed Medical. ### Funding Statement This study was supported by NIH K08DK118176. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Cedars-Sinai Medical Center Institutional Review Board provided approval for the study (IRB#00040261). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors. * (LUTS) : Lower urinary tract urinary symptoms (OAB) : overactive bladder (IC/BPS) : interstitial cystitis/painful bladder syndrome (ML) : machine learning (POP) : pelvic organ prolapse (fGUPI) : female Genitourinary Pain Index (OABq : Pelvic Floor Distress Inventory (PFDI-20) : Overactive Bladder Questionnaire (ICSI/ICPI) : Interstitial Cystitis Symptom and Problem Indices (UTI) : urinary tract infection (UMAP) : Uniform manifold approximation and projection (UUI) : (pelvic floor disorders [PFD]), urgency urinary incontinence (MUI) : mixed urinary incontinence (FI/MUI) : fecal incontinence/mixed urinary incontinence (MFS) : myofascial frequency syndrome (POP) : pelvic organ prolapse (MPP) : myofascial pelvic pain (Controls/SUI) : controls/stress urinary incontinence (NUPP) : non-urologic pelvic pain (SUI) : stress urinary incontinence (UUI) : urgency urinary incontinence