The invention discloses a vasculopathy
risk identification system based on a
machine learning model. A control host acquires a blood
oxygen value, a pH value, a
lactic acid value and a pressure value in
blood vessel blood of an evaluator and a
blood vessel inner wall structure image; the
blood vessel is a
vein blood vessel, analyzing the
tube diameter and the thickness of the intima, the medium and the
adventitia based on the structure image, inputting the blood
oxygen value, the pH value, the
lactic acid value, the pressure value, the
tube diameter and the thickness of the intima, the medium and the
adventitia of the assessor into a target
vein blood vessel
machine learning model, and training and outputting a
vein blood vessel
lesion risk identification result; the blood vessel is an
artery blood vessel, and the composition and form of the
artery blood vessel are identified and the
stenosis degree value of the
vessel diameter is calculated when the
vessel diameter, the thickness of the inner, middle and outer membranes and
artery plaques exist in the artery blood vessel are analyzed based on the structure image; and inputting the blood
oxygen value, the pH value, the
lactic acid value, the pressure value, the
tube diameter, the thickness of the intima, the middle and the
adventitia of the blood vessel, the composition and the form of the artery plaque and the tube
diameter stenosis degree value of the assessor into a target artery blood vessel
machine learning model to
train and output an artery blood vessel
lesion risk identification result.