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Analyzing Cardiovascular Risk Factors with R

In this project, we’ll learn to analyze and understand different risk factors that may cause cardiovascular diseases using machine learning models.

Analyzing Cardiovascular Risk Factors with R

You will learn to:

Load and process the data frame in R.

Preprocess, analyze, and explore datasets using R.

Apply GLM on datasets after preprocessing using R.

Evaluate GLM accuracy.


Data Cleaning

Data Visualization

Data Analysis


Intermediate coding skills in R

Intermediate knowledge about plotting graphs in R

Basic understanding of statistical tools

Basic understanding of the general linear model



Project Description

In this project, we’ll start with raw data, perform preprocessing, and then utilize various machine learning techniques to extract binary outcomes from the data, explaining which factors actually have a role to play in cardiac diseases and which don't.

We have a dataset consisting of around 70,000 records that contain various heart-related data for different individuals. This data includes indicators such as whether they are cardiac patients, their smoking status, activity level, blood pressure condition, and other relevant information.

We'll conduct an analysis of various habits among individuals based on the available data to determine whether they are cardiac patients or not. We'll apply chi-squared tests and develop new insights from the data to help us analyze better. We'll create a model for making a conclusive argument about our analysis.

Project Tasks


Data Preprocessing

Task 0: Description of the Dataset

Task 1: Import the Dataset and Libraries

Task 2: Process the Data for Analysis

Task 3: Calculate the BMI and MAP


Data Analysis

Task 4: The Effects of Alcohol Consumption on Cardiac Disease

Task 5: The Effects of Activity on Cardiac Disease

Task 6: Analysis of Smoking Habits and Cardiac Disease

Task 7: Create a Composite Plot

Task 8: The Effects of Gender on Cardiac Disease

Task 9: Effects of Cholesterol on Cardiac Disease

Task 10: Effects of Glucose on Cardiac Disease

Task 11: Composite Plot of Cholesterol and Glucose

Task 12: The Effects of Age on Cardiac Disease

Task 13: Interquartile Range Method

Task 14: Effects of BMI on Cardiac Disease

Task 15: Effects of MAP on Cardiac Disease


Evaluation of Model

Task 16: General Linear Model

Task 17: Accuracy of Model