Using machine learning to compare the probability of success of two different treatment strategies

I would like to preface by saying I am relatively new in the world of machine learning, however I have a decent background in statistics. I have a large database of patients who underwent a certain procedure. This database contains patient information, as well as the outcome of the procedure (success or failure). There are two different treatment strategies used for this type of procedure, let's call them A and B. What I want to do is create "calculator" that will take user input on the various patient characteristics and then output the strategy that has the highest chance of success for this particular patient. For example, let's say we have 3 patient characteristics (there are more in the actual database): age, gender, and BMI. User input: Age = 58 Gender = Male BMI = 28 Output: Strategy A has a better chance of success I came up with a potential way to do this, by creating two models (eg logistic regression), one for each strategy, and training them and testing them using my database. Then I will get user input for the patient characteristics and use those in each model to get the probability of success for each strategy (using model.predict_proba in python). Lastly, I will compare the two probabilities and suggest the strategy with the highest value. I am not sure if this is a correct way of approaching this problem, or if there is a better method. Thank you in advance for the help!