Machine Learning and Urinary Incontinence in Prostate Cancer: A Generalized Additive Model of Physical Activity and Recovery Patterns

Abstract

The ASCAPE project aims to improve the health-related quality of life of prostate cancer patients using artificial intelligence-driven solutions. This study tries to unravel the complex relationships between patient data variables and urinary incontinence (UI), and post-radical prostatectomy using the ASCAPE datasets. We employed a Generalized Additive Model to analyze patient-reported outcomes on UI (QLQ PR25 questionnaires over a 12-month period), and objective data derived from wearable devices. Our findings showcase age and comorbidities as the main predictors of incontinence severity, whereas physical activity failed to show any significance in our model. Our study highlights the importance of a personalized approach to incontinence care, where patient characteristics and recovery patterns are considered when developing treatment plans.