I was reading many machine learning questions like this one but I am not sure how to apply them to my scenario. I come from a biology/medicine background, and my math knowledge is limited (last thing I learned was calculus long ago), and some of the explanations on this site go over my head.
Suppose someone is filling out forms everyday. The form contains various fields (patient information, medical history, job, etc.) Now suppose there are
n patients coming to the hospital each day (so that's a minimum of
n forms filled out each day, each time they arrive). Some parts of the form may never change (e.g. gender, race, etc. as an example) but some parts may (e.g. the medical history, checkup time, etc). Now I need to write a program that "learns" from this data so I can anticipate when the patient is likely to arrive again, then I need to determine whether the form is likely to change based on how often it changed in the past, and if the "important" parts (like the medical history) is unlikely to change, then I need to generate a form in anticipation of the event.
Things I've considered
With whatever limited knowledge I've got, I figured I'd need to calculate some kind of variance to determine how different each form is from other forms of each patient. Then I figured I'd be pushing the data through some unsupervised learning 'thing' to detect if some kind of pattern exists to the patients' arrivals, and if it does then I can use the past data on the variance to determine if a form should be generated in advance.
So, I assume I'll need multiple algorithms (different for each part of the question), and I've looked at Naive Bayes, Logistic Regression, Decision Trees, and SVMs, but I hit my head on complex math and nearly suffered an aneurysm (joking)... To begin this quest, which model/algorithm should I start with, and why does it work well for this scenario over the others? I would appreciate a more wordy (less math) answer.