# Multiple variable as input and output

I'm trying to predict the possible diagnosis given a consultation reason. I have ID's for all the data. So my data kind of looks like below

Reason            | Diagnosis
------------------------------
448, 124          | 9
551, 448, 122     | 9, 12
111, 110          | 32
143               | 43


There can be up to 10 reasons and upto 5 diagnoses in my training data.

What I'm looking for in the algorithm or model is that it accepts 1 - 10 reason_ids as input and returns top 5 possibilities for diagnosis with % of probability.

I'm good at python so if there is any open source model or code I can look at it will be great.

• Use a multi-class multi-label classifier, let the diagnosis be the output classes, and the reason be the binary inputs (0 if the reason does not apply). Use this. – Emre Sep 14 '17 at 17:12