| Abstract | Stroke is one of the leading causes of death in the world with the number of
people suffering from it increasing every year. Ischemic strokes, one of the two main
types of stroke, occur when blood clots block brain arteries which leads to infarction
eventually leading to brain edema. If not addressed quickly enough it may lead to
disability and in worst case scenario may even lead to death.
In this thesis we proposed a machine learning based MATLAB tool that aids in
speeding up the prognosis of acute ischemic stroke patients. From a set of patient
medical data such as patient age, blood pressure reading and infarction volume from
first CT scan, we created three prediction models which predict second infarction
volume, decision for surgery and treatment time. We also experimented with utilizing
the technique of feature reduction and implementing Fuzzy Inference System to
consider improving the generated models and combined the best performing models
into a MATLAB application. |
