A team of scientists has been studying patients who might have a dangerous disease.
Medication is available but has serious side-eects, so it is important to screen patients before they are given
the drug. The only known test, unfortunately, is very expensive, but it is known that some of the genes
implicated in the disease are also involved in the regulation of blood pressure and blood sodium levels, and
there is hope that it may be possible to screen subjects based on these two easily measured quantities. A
database of sodium levels, (L) and blood pressure (P) has been compiled, with patients labeled as positive
(D=1) or negative (D=0) for the disease based on the expensive test. Your task is to design a simple classifier
that can classify future subjects with unknown labels correctly.
You will try three dierent approaches to the task:
1. A minimum distance classier.
2. An aumented k-nearest neighbors classier.
3. A single perceptron.
The augmented k-NN classier will use one or more heuristics, such as using a customized k value for each
point based on some criterion (e.g., at least 60% of neighbors in the winning class), or using distance weights.
The choice of augmentation is up to you. You can think of it yourself or search in the literature for options.
You should write programs that implement each classier and run them on the data set. You should use the
available data in a way that validates the ability of the classiers to classify novel
This can be done using any coding language.
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Hello, I'm an experienced programmer with knowledge of Machine Learning and classification algorithms. You're task in fairly simple, so it will not take much time to do it.