Predicting Heart Disease Using Regression Analysis.

As per the Centers for Disease Control and Prevention report, heart disease is the prime killer of both men and women in the United States and around the globe. There are several data mining techniques that can be leveraged by researchers/ statisticians to help health care professionals determine heart disease and its potential causes. Some of the significant risk factors associated with heart disease are age, blood pressure, total cholesterol, diabetes, hypertension, family history of heart disease, obesity, lack of physical exercise, etc.

In this project from Data Camp, the objective of my project is to build a regression model and run statistical tests to assess how strongly are the clinical factors associated with heart disease and how it is related to the higher probability of getting a heart disease. I shall be implementing Multiple and Logistic Regression approaches together with data explorations in ggplot and dplyr. This project uses the Cleveland heart disease dataset.