kevinkrzys / prediction-of-heart-attack

This project uses the Heart Disease Data Set from the UCI Machine Learning Repository to identify major contributors of Heart Attack and to predict patients with a high chance of having Heart Attack.

Prediction of Heart Attack

This project uses the Heart Disease Data Set from the UCI Machine Learning Repository to identify major contributors of Heart Attack and to predict patients with a high chance of having Heart Attack.

Survey

We surveyed 84 people to find out what they thought were the major contributors to Heart Attack and here is what they said:

72.6% said Cholesterol whereas 48.8% said Age

But are these really the biggest contributors to Heart Attack? This project aims to explore exactly that

Models Used:

1. Logistic Regression
2. StepAIC
3. Decision Tree

Major Contributors Identified

1. Values for Thalassemia (Normal/Fixed Defect/Reversable Defect)
2. The number of major vessels colored by Fluoroscopy (0 - 3)
3. Maximum heart rate achieved
4. Chest pain type (Typical Angina/Atypical Angina/Non-Anginal Pain/Asymptomatic)

We achieved an accuracy of 86.67% using the Decision Tree Model