Using random forest to learn Imbalanced Data (rare disease) I met a question when I ran the random forest. I used "V1", "V2", "V3" to predict a binary outcome (1: sick; 0: no) with random forest.

I got a very high accuracy score (99%) however, when I check the confusion matrix, it shows that none of sick individuals were caught in testing data set (30% of entire data set). Here is the confusion matrix:

[[856   0]

[  9   0]]

This result means that 0 out of 9 people was detected as sick and it causes my attention. Maybe because the data set is imbalanced (very few sick individuals)?

I would like to see if there is any other ways to detect sick individuals rather than a high accuracy rate, which means it is OK it has higher false positive rate but I would like to catch all 9 (true positive) individuals.

Thanks!
Use class weights, to weight errors, so that "incorrectly labelling a sick person as healthy" is penalized more than "incorrectly labelling a healthy person as sick".  Or look up any of the other standard techniques for dealing with class imbalance.