Statistical Model to Predict and Prevent the Occurrence of Urinary Incontinence

摘要

Urinary Incontinence (UI) is the inability to completely control the process of releasing urine. Although urinary incontinence affects people of all ages, it is more disturbing and embarrassing for adults. Studies have shown that there is a considerable number of urinary incontinence patients in the United States. The actual number could be significantly bigger because most adults would not report the issue and seek treatment. Treating this issue can be costly because it includes pediatrician visits, urologist visits, psychologist visits, taking medicines, and the effort of dealing with the symptoms. UI is a socially embarrassing and psychologically distressing disorder. It can interfere with the work, school, and social life of the affected individual as well as family stability. The aim of this research is to (1) develop a forecasting model to predict the next time a person needs to go to the bathroom; (2) build an app for patients to use and keep track of future bathroom trip times. Data about age, gender, physical activity, inputs (liquid consumption), time of consumption, outputs (urine volumes), and time of release were collected to create a statistical predictive model that would allow the person to know when they will need to make the next bathroom trip. Eventually, they can prevent its occurrence and enjoy a better quality of life. A model has been developed using the data collected from participants. The model can predict the next future time a person needs to go to the restroom with accuracy of at least 80% based on the individual's gender, age, weight, physical activity average urine volume, liquid consumption, and urine collection rate.