I am doing a project related to predicting the next glucose value based on his/her past records. But in some patients, some of the recordings are missing. There are 2 scenarios of how the blood glucose values are missing in my dataset. So I'll label them as Scenario 1 and Scenario 2 in my examples.
Scenario - 1
- A patient has recorded the glucose values thrice a day basically before breakfast, lunch and dinner on day 30. But on day 31 he has only recorded only the breakfast and dinner. Lunch is not recorded on day 31. So how can we replace the 0 value in lunch with another value?
Scenario - 2
- Another patient has recorded the blood glucose values continuously from day 1 to day 40, and then he hasn't recorded the blood glucose values for another 2 days (day 41 and day 42 recordings are not there for before breakfast, before lunch and before dinner). Again he started recording the values in the day 43. So what is the best approach to tackle this kind of scenario?
I went through many articles and majority explained on replacing the mode, median or mean values for empty records. But I think mode, median is not suitable for this kind of dataset. I highly doubt whether I can use the mean also to replace values with empty records in the Scenario - 1. Can we actually use mean for replacing the empty records in Scenario - 1 or is there any other good approach?
From my knowledge I think I can't use the above three methods in replacing the values in Scenario - 2 since about 6 records are missing. If I am correct what is the best approach for the Scenario - 2.