# Data Mining, generating a model from a database

I have a database with different variables which contains information such as age, date of vaccination, number of doses as well as the number of antibodies (which is my target variable). If the number of antibodies is under 100 and 10, the patient's antibodies level is regarded as low and very low accordingly. My goal is to create a model by which I would be able to determine WHEN a patient should receive his next vaccination based on these facts. Thus far I have only generated decision trees using R studio. My mathematical knowledge is limited to come up with a solution. How could this be achieved?

• If you're trying to predict dangerously low levels of antibodies, and you have continuous count data on antibodies, it would be better to model the continuous data as your outcome variable rather than the acceptable / low / very low binned version of that variable. (This is not binary BTW, so you would need ordinal logistic regression if you were to use this binned variable, despite the waste of information.) However, it's unclear to me whether you're really interested in simply predicting antibody count or whether you're trying to model longitudinal change, which is much more complex. – Nick Stauner Jul 2 '14 at 20:45
• I think this question relates to supervised learning more than data mining (tags are inadequate). As for your question: asked that way, there are many ways of building a predictor. The right one to pick really depends on your data. Start with some reading on supervised learning: en.wikipedia.org/wiki/Supervised_learning ; if you don't know what to pick, you may try several ways and evaluate the performance of each method from a subset of your data (test database) not used in the learning process. Anyway, as @NickStauner suggests, predict the number of antibodies, not the binned version – Romain Reboulleau Oct 2 '18 at 15:50