Predicting three related scores with one joint model 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:


Scale of Happy 1 till 7
Scale of depression 1 till 7
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.
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: