Table 2 Potential features from participants’ self-records for the machine learning model
From: Mitigating urinary incontinence condition using machine learning
Input data | Type of drinks | Demographic data | Number of drinks | Output data |
|---|---|---|---|---|
Liquid volume | Alcohol | Age | Exercise level | Urination time |
Number of drinks | Coffee | Youth/adult/senior | Exercise per week | |
Time of consumption | Juice | Gender | ||
Milk | Weight | |||
Soda | Height | |||
Water | BMI |