A simple approach to solving multi-label classification problems
One of the most practical and easy ways in multi-label classification problems is to turn the problem into a binary classification problem for each class. For each class, we train the model with the training data set and test whether the data set we have (based on patient information) belongs to the relevant disease (class). When it is understood whether the data set for each class is only related to that dataset, patients ( instances) associated with more than one disease (label) can also be assigned to more than one class. We call this method converting the multi-label classification problem into a multiple binary classification problem.
