Architecture design to find the most relevant symptoms given an input symptom
You could use scikit learn library to build a predictive model where the classifier is made up of the symptoms and the labels as the disease. You can then analyse which symptoms contribute most to the disease.
My goal is to build a symptom recommendation system
I have 3 columns of data in my excel.

Patient id
Symptoms
Disease detection

For each patient id there is one or more than one symptom that leads to a disease detection. My goal is to find the most relevant symptoms given an input symptom.
I am unable to think of a way to come up with a plan given the data limitation. One idea I have is to transform the data into a matrix with all symptoms as columns and disease as rows. For each disease if there is a symptom mark 1 else put 0 for all other symptoms. Will this approach work? Any idea on how to design this system