This sounds like an interesting project. I would like to add these additional thoughts
- try to interview people who are familiar with this no-show process and might have some gut ideas about what is driving this and see if you get these ideas into data. Often my experience with data mining and models is you would not detect something spectacular new but rather develop systematic rules and implement them on a large scale that experts know by instinct or could write down by hand. So basically you distill a lot of human knowledge and experience into a (easily reproducible) predictive model. Also you should show appreciation and some humility towards the people doing the tasks on hotlines and front desks - in the end your job as a data scientist/analyst is to make their work and life better and more efficient
- seasonality might indeed play an important role. You might want to include a lot of this information about holidays or upcoming local events, concerts and the like. Be aware they might shift in time (like easter) so maybe you want to include them like days-before-procedure (Dividing and splitting rows)
- when you already have a system in place to call possible no-shows up front you might use a score to only call the 10% most likely no-shows and so save on precious time of nurses. You might include the answers and the learnings from the people you did not call and who still turned out to be no-shows into a separate model
- one other way to improve the efficiency of calls is to use uplift-modelling for which there even is a KNIME extension (https://www.knime.com/book/dymatrix-uplift-modeling-nodes-for-knime-trusted-extension). But it takes some effort to implement that. So you do not call the people who are already lining up in front of the Apple store to buy the latest iPhone but rather the ones your call might convince to switch there from Android (without your call they would have stayed with Google’s system)
- try to get as much information about the patients as you could (and are allowed by data privacy regulations). Age, martial status, kids all might play a role - either as a ‘real’ reason or to use Auntie Jane’s wedding anniversary to chicken out of a procedure. And I would assume you have some geographical and traffic information there (ease to reach the medical facility)
And on a general note. My impression is that the H2O.ai nodes / model implementations are doing a really great job at building models once you have your data ready (https://www.knime.com/nodeguide/analytics/h2o-machine-learning).