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

Abstract

AbstractObjectiveLower 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.MethodsQuestionnaire 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.ResultsThis 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.ConclusionsWe 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.