Adaptive Prediction Modeling

I’m not sure if it will actually work in practice, but I have a loose adaptive predictive model I’m currently brainstorming:

A is either researched input or random input.
B can be taken as a decision tree or predictive model.
C is an adapted conditional from result of B.
D is a direct result from C.
Global else happen when danger exceeds danger threshold.
The backed up repo is taken is a new seedling, and sprouted.

If danger_level high
  Auto clone neuron to previous direction.
  Encrypt old processing.
  If a is word, definition, or related topics, adapt b to word, definition, or related topic:
    If prediction b is a word, c is send to word document.
      perform d
    Else if b is a definition, c is send to definition document.
      perform d
    Else if b is related topics, c is send to related topics document.
      perform d
  Check for danger
    if danger exceeds threshold
      go to high danger processing
      repeat process

Is there any way I could maybe improve it?

Also, the idea is that if a certain amount of danger reaches a specified threshold, it goes into automatic cloning and encryption mode.