Predicting three related scores with one joint model

Question

I have multiple features and I want to predict three outcome scores.

Features:

• Length in cm smallest is 40cm biggest is 209cm
• Kilo: 39 till 302
• Age: 19 till 111
• Gender: Male, female, transgender
• Diagnoses: numbers of different diseases
• Medicines: numbers of different medicines
• Urine level: 0 till 5

Scores:

1. Scale of Happy 1 till 7
2. Scale of depression 1 till 7
3. Scale of health 1 till 7

I know I can use supervised learning and create models where I predict the scores individually. I already pre-trained three different models.

Do you have some feedback?

Happiness, depression and health are related. Are there algorithms that can deal with this? Based on historical data (the features and predictions) and the trained model I want to predict the scores for new patients based on their features and historical scores.

Answer 1

Do you have a labeled set? Any algorithm can do it in a supervised manner, as long as you have enough labeled data to train it on.

A tree-based model (Random Forests Classifier for instance) could do really well on this task, especially since you have categorical data. All you need to do is encode the data as numbers (for instance 0: male, 1: female, 2: transgender) and feed it into your model (applying random splits and cross-validation and all that).

In case you don't, then the best you can do without collecting new data is to find clusters of related patients in your dataset. The same rules for encoding variables apply, although you may have to pay some attention to how you scale your variables since clustering algorithms are sensitive to relative scaling of features. This happens because most clustering algorithms cluster data using distance metrics which lose meaning when you are dealing with categories. See this for more info on clustering categorical data:

Answer 2

You can try the cascade model, e.g. you start by fitting three individual models

S1=F(x1,...,x7)

S2=F(x1,...,x7)

S3=F(x1,...,x7)

Next, you use the outcomes of these individual models to fit the second level models, e.g.

M1=F(x1,...,x7,S1,S2)

Or

M2=F(x1,...,x7,S1,S3) ...

The structure and order of fitting the models depends on your data.

You can either test the performance of some combinations or just set the structure based on expert judgement.

Of course, there is no guarantee that the performance of these stacked models will be better the performance of your individual models.