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Naive Bayes classifier

A project log for F°LUEX

An intelligent clinical thermometer that can distinguish between colds and flu

M. BindhammerM. Bindhammer 02/26/2020 at 14:120 Comments

In this log I will explain how I use a naive Bayes classifier to distinguish between flu and common cold by just knowing a few symptoms.

A naive Bayes classifier is based on applying Bayes' theorem with the naive assumption that the data are independent from each other. Bayes' theorem can be written as

where

h: Hypothesis,
d: Data,
P(h): Probability of hypothesis h before seeing any data d and
P(d|h): Probability of the data if the hypothesis h is true.

The data evidence is given by

where P(h|d) is the probability of hypothesis h after having seen the data d.

Generally we want the most probable hypothesis given training data. This is the maximum a posteriori hypothesis

where H is the hypothesis set or space.

If our data d has several attributes, the naive Bayes assumption can be used. Attributes a that describe data instances are conditionally independent given the classification hypothesis

I didn't get any data how many people catching a common cold a year, but this can easily be fixed. I know that every human depending on the age catches a cold 3-15 times a year. Taking the average of 9 times a year and assuming a world population of 7·109, we have 63·109 common cold cases a year. Around 5·106 people will get the flu per year. This lead as to the following probabilities:

That means only one of approx. 12500 patients with common cold/flu like symptoms has actually flu!

Next we need to know more about the symptoms. How often occurs a certain symptom when you have common cold or flu? After a bit of search, I got the needed data, see here, and we can derive our probability-look-up table for supervised learning:

ProbFluCommon cold
P(h)0.000080.99992
P(Fatigue|h)0.80.225
P(Fever|h)0.90.005
P(Chills|h)0.90.1
P(Sore throat|h)0.550.5
P(Cough|h)0.90.4
P(Headache|h)0.850.25
P(Muscle pain|h)0.6750.1
P(Sneezing|h)0.250.9

Finally we can compute our classification hypothesis:

You certainly know this, but let's mention it on that point: the probability that an event A is not occurring is given by

Multiplying a lot of probabilities, which are between 0 and 1 by definition, can result in floating-point underflow, but this can be avoided easily. Since

we can just summing logs of probabilities rather than multiplying probabilities. The class with highest final un-normalized log probability score is still the most probable. Hence

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