Why to exclude features used for label generation during modeling? I have a dataset like below without labels



But with the help of experts opinion, we generate labels based on the below 3 rules (all 3 rules has to be met to label it as 1)



So now the dataset looks like below



As you can see that my final dataset has the labels.

Now I can run a ML model for classification. Am I right?

But I read that during model building process, features that were used to create the labels will have to be excluded because they might result in perfect separation of classes and model might fail. what does it mean by fail? Aren't we aiming for separation of classes through classification algorithms?

May I know why do we have to exclude these features (Ex: RG, FG and BP features which were used to derive labels)?

It's basically my model will be built on below dataset. But aren't we losing the predictive power? why do we have to build a model by excluding those features (that were used to derive labels)?
What you are doing is right. You can build a ML model using this. In your case your input and output are correlated. Consider the salary of an employee wrt his experience. These both are related and sometimes used to derive salary based on experience.   

What you might have read is if one feature is used to derive other feature and dont use both features, as both will yield same results. You can point to that writing for further clarification.   

Additionally, what is the significance of variables T1,T2 and T3? If these are nowhere correlated to the output label then actually you would have to eliminate them.