I need help with planning and ideas on how to build an data table for a machine learning model in sklearn.
The model is supposed to help the clinic manager decide which treatment method and therapist best fits a specific patient according to past experience with other patients.
The input that the model will receive in prediction is the patient's details (personal details, etc.) the model should propose the most appropriate treatment method and therapist.
The data I have to train the model is:
- patient details
- selected treatment method
- selected therapist
- feedback on the patient's progress (how the patient progressed / retreated)
My question is how to organize the table - which columns will be the attributes? What will be the tag? And how do I get classification for both preferred treatment method and therapist? (Allegedly there are 2 tags here)
We would be happy to receive any ideas
thanks for the reply
In the first stage, all use of the model will be at the same time together with human judgment and decision. And not in their place. So that significant implications will not be at this stage.
In order not to rely on incorrect previous data, I have a column in the table that shows what the success of the specific treatment was - and of course I would like the model to give a significant ratio to this column.
I have some ideas on how to do this but all of them have "bugs" at certain points.
My main question is how to decide what will be the target column in the table and then the rest of course will be the attributes.
whether the method and the therapist or the progress - and then in model will be a regression model. (The progress column is a numerical value)
something else -I have not found in my search examples of using a double target- I would be happy if you can direct me to code samples (if any)