Heart Disease Detection

Heart Disease continues to be the leading cause of death worldwide, many of which could be prevented if detected early enough. The goal of this project is to attempt to utilize machine learning to detect the presence of heart disease in an individual.

I completed this project to learn more about different machine learning models and to become familiar with using Scikit-learn libraries, data preprocessing, and model evaluation. The dataset used is the Cleveland Heart Disease database from the UCI Repository. This dataset uses a subset of 14 out of originally 76 attributes that is part of a much larger dataset. Of the 14 attributes, 13 will be used as characteristic predictors for classifying whether or not there is presence of heart disease.

The IPYNB file can be found on my Github which is linked on the Home page.