I'm trying to come up with the right algorithm for a system in which the user enters a few symptoms and the system has to predict or determine the likelihood that a few selected symptoms are associated with those existing in the system. Then after associating them, the result or output should be a specific disease for the symptoms.
The system is comprised of a series of diseases with each assigned to specific symptoms, which also exist in the system.
Let's assume that the user entered the following input:
A, B, C, and D
The first thing the system should do is check and associate each symptom (in this case represented by alphabetical letters) individually against a data-table of symptoms that already exist. And in cases where the input doesn't exist, the system should report or send feedback about it.
And also, let's say that
A and B was in the data-table, so we are 100% sure that they're valid or exist and the system is able to give out the disease based on the input. Then let's say that the input now is
C and D where
C doesn't exist in the data-table, but there is a possibility that
We don't give
D a score of 100%, but maybe something lower (let's say 90%). Then
C just doesn't exist at all in the data-table. So,
C gets a score of 0%.
Therefore, the system should have some kind of association and prediction techniques or rules to output the result by judging the user's input.
Summary of generating the output:
If A and B were entered and exist, then output = 100% If D was entered and existed but C was not, then output = 90% If all entered don't exist, then output = 0%
What techniques would be used to produce this system?