I'm currently working on a small project using the D1NAMO dataset (1). I want to predict the glucose level (that is given in the dataset) based on several features: accelerometer data, heartbeat (ECG) data, breathing data and some other features. I have several users (006, 007, 008 etc) that all have a few days of data.
The problem is that the target value (glucose) is measured only once in 5 minutes, whereas the other measurements are done every few milliseconds. That means that in my data I have many rows without the target value. For example, for one user (008) I have 214 rows where I have a glucose level and around 60.000 rows where glucose is NaN. I want to use a model without the notion of time, so that could be anything such as FFNN, CNN, Decision Trees (XGBoost), ensembles etc.
- How should I deal with this problem where I have only few target values? Should I summarize all values in a window of 5 minutes to match with the target value?
- Should I make a model on the instance level or population level?
Thanks in advance for any replies.