PhD student within health care in need of some guidance..

I’m planning a project aiming to classify disease. I’ve been told by my supervisors to create a “manual” decision tree together with an expertgroup (Basically a expert system). This, I’m told, will then constitute the foundation on which I later “add” machine learning capabilities. The addition is currently intended to be a DL model able to identify certain waveforms. As I’m reading and getting a grip of ML, this seem counterproductive. Neither of the currently contributing supervisors know ML why I’m curious to get some input here..

1. Is there any sort of requirement building a initial decision tree to later integrate ML models upon? I’m under the impression that a decision tree (CART or similar) is built in a process not requiring a expert group (however experts could be relevant for reviewing the model, but that’s another conversation)

2. Is there a widely known resource dictating steps for building a decision tree?