Machine learning models for identifying urinary incontinence in women with a history of hysterectomy using basic demographic and clinical characteristics: A cross-sectional study

Key Points

• Urinary incontinence was identified with a machine learning model that utilized clinical characteristics and demographics.
• The model achieved a sensitivity of 83% in identifying urinary incontinence among participants.
• Analysis involved demographic and clinical characteristics from women with a history of hysterectomy across a single hospital setting.
• Results imply that machine learning could enhance detection of urinary incontinence, highlighting the need for better diagnostic protocols.