Naive Bayes method

Question

Let's assume that I've got patients with information about their diseases and symptoms. I want to estimate probability of P(disease_i = TRUE|symptom_j = TRUE). I suppose that I should use NB classifier, but every example I've found apply Naive Bayes when there's only one disease (like predicting the probability of heart attack).

My data look like below:

patient | disease | if_disease_present | symptom

1       | d1      | TRUE               | s1
2       | d1      | FALSE              | s2
3       | d2      | TRUE               | s1
4       | d3      | TRUE               | s4
5       | d4      | FALSE              | s8
...

My idea was to split data according to diseases and build the number of naive Bayesian models how many unique diseases I have in my data, but I have doubts if it's proper method.

Answer 1

If you want to predict the disease, don't split the data on it.

That is your target variable!

But as is, your table is not suitable for this task. You need to preprocess it, probably do some pivotization.