Algorithm Development Steps for a predictive model for nocturnal repositioning of critical care patients based on regular sleep body mobilization patterns using Machine Learning

A repositioning frequency algorithm was designed to monitor normal individuals during the sleep period considering the two input variables with quantitative data (BMI and age), to derive a decision tree to define the position changes suggested by age groups (group 1 adults: 18-45 years and group No 2_Older adults: ≥ 46 years), and according to the BMI (Group 1. Normal: 18.5 to 24.9; Group 2 overweight: ≥ 25) during sleep time, using Python language and the scikit-learn.org library.