In a supervised cancer classification task which is given the data containing metrics we want to classify whether the patient has cancer or is at high risk (label 1) or low risk (0). However, there is another attribute which is
screening status which I want to include in the prediction. But I don't know how -- should it be a label or an input feature? The information on patients screened have the screening year as the value. Those who were not screened have the value for the year empty. Let the label for
is screened be 3 and
not screened be 4. So, the idea is:
-- a patient is at high risk and was screened : [1,3] -- patient is at high risk but not screened: [1,4] -- patient is at low risk and was screened: [0,3] -- patient is at low risk but not screened: [0,4]
Basically, I want to assess the importance of early screening in predicting benign/malignant. The patients who have been screened early may not have cancer. On the other hand, it may happen that those who were screened developed cancer in later stages. This implies that the patients who did not pass the screening tests were diagnosed with cancer. So, what are the factors that influence the patient to pass or reject the screening test and whether the screening test itself can help in predicting/classification of malignant/benign. This is the research problem.
Confusion 1: How can I include this
screening status? Will the information on screening be included as a label ?
Confusion 2: Will it be multi-label or multi-class classification?
Is my approach correct? Please let me know if I am in the correct direction an please correct me otherwise. Links to papers with similar problem will also be helpful.