The algorithms you take to set up your machine are based on your personal preferences, but for the sake of information we will explain
how each one works. It's always better to choose if you have a proper understanding of why you're making your choices.
The use of the first algorithm that you will have to choose is to reduce the amount of data that your machine will have to process, because even if the
machine would become more accurate by getting more data, it would take way too long to process it all. So what this algorithm will do
is take the most important variables and get rid of the rest.
You can choose the "tactic" it uses to do so:·
·Correlation, that finds the variables that usually change together and groups them into one unique variable.
·Principal Component Analysis, which chooses the data that affects the result the most.
·Linear Discriminant Analysis, which finds a new dimension in which to fit different kinds of data as one.
The second algorithm that you choose will be the one that teaches the machine how to interpret the data that we give it. This way, when the machine is given
a new MRI that isn't solved, it will have learnt from the ones that were used to teach it, and it will know how to diagnose the patient based
on what it has seen in other MRIs.
For this step, you can choose between 4 optons:
·Support Vector Machine, which creates a threshold between groups of patients (ill/healthy, etc).
·Random Forest, that randomly creates many decision trees which "vote" their outcome, picking the one with the most votes.
·Neural Network, that immitates the structure of a human brain with several layers of "neurons".
·Generalized Linear Model, that classifies patients based on a logistic regression.