An Algorithm to Predict the Probability of Heart Disease from Cardiovascular Results
[This capstone project is part of the Data Analysis & Interpretation Specialization program by Wesleyan University on Coursera.]
The purpose of this study is to identify the best predictors of the presence of heart disease using multiple health and demographic factors such as results from an electrocardiographic (ECG) test, the presence of exercise-induced angina (i.e. chest pain caused by reduced blood flow to the heart), age, and sex. The end-result of this project is an algorithmic model that predicts the probability of the presence of heart disease in a patient.
As a prospective graduate student in Data Analytics, and as someone deeply interested in health and nutrition, this is an opportunity for me to apply my analytical skills in an area that is of interest to me.
Being able to predict the probability of the presence of heart disease within a patient can lead to earlier action being taken in remedying the situation, which would translate to more lives saved and lower healthcare costs.