New to data science and am trying to be a self-starter and implement advanced data analytics in my subspecialty of surgery. Below is a description of my data set. I know that I will have to explore multiple methods, but wanted to get your take on which you think may be best. I will most likely be using R to achieve this analysis.
- Have a data set with about 200 patients (rows)
- Each patient has about 10-15 variables (preoperative and intraoperative)
- Each patient has undergone either nonoperative or operative management
- Success in nonoperative or operative management is determined by a questionnaire that patients fill out 1 year after they are seen. This questionnaire gives a binary outcome on whether they (1) Benefited or (2) Did not Benefit from the surgery.
My questions for the study are as follows:
- In the surgery group, I am trying to find out which variables lead to patients (1) Benefit vs (2) Do not benefit from surgery, and create a model which can better help predict which patients we can operate on (I have left out some details such as patient population, type of surgery, etc).
- In the second study, I would like to determine which patients we should operate on. In other words, I would like to find out which preoperative characteristics make some patients more likely to benefit from (1) Operative treatment vs (2) Nonoperative treatment and in this case the outcome will also be binary from the questionnaire.
I have tried linear and logistic regressions for this which have not been very good, hence why I am trying to learn more advanced models.
Models which are easier to comprehend by clinicians are more valuable which is why I haven't delved into neural nets. I appreciate any and all advice that can be provided. In addition, if I expand this data set to 600 people, would you use another model? I don't have access to large servers so most of this will be done on my laptop though I can use online resources if necessary (Azure etc).
Thank you all for your help and input.