Doctors frequently study former cases to learn how to best treat their patients. A patient who has a similar health history or symptoms to a previous patient could benefit from undergoing the same treatment. This project investigates whether doctors might be able to group together patients to target treatments using common unsupervised learning techniques.
This project experiments with clustering algorithms to help doctors inform treatment for heart disease patients and uses k-means and hierarchical clustering algorithms.
The dataset for this project contains characteristics of patients diagnosed with heart disease. It can be found in dataset folder or here.