Introduction

Coronary artery disease is a heart condition involving a reduction of blood flow into the heart caused by the narrowing of blood vessels due to the build up of atherosclerotic plaque. This heart disease is the most common type and if blockage becomes severe enough can lead to heart attacks/failure.

The goal of this project is to build a model using classification with logistic regression to predict the probability of coronary heart disease in a patient.

Source for the data used in this project is from Heart Disease Dataset from the UCI Machine Learning Repository. Dataset contains the following features:

Predictive Variables

• age: age in years

• sex: gender

  • Value 0: female
  • Value 1: male

• cp: chest pain type

  • Value 1: typical angina
  • Value 2: atypical angina
  • Value 3: non-anginal pain
  • Value 4: asymptomatic

• trestpbs: resting blood pressure (in mm Hg on admission to the hospital)

• chol: serum cholestoral in mg/dl

• fbs: (fasting blood sugar > 120 mg/dl)

  * Value 0: false
  * Value 1: true
  

• restecg: resting electrocardiographic results

  *  Value 0: normal
  *  Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
  *  Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria
  

• thalach: maximum heart rate achieved

• exang: exercise induced angina

  * Value 0: no
  *  Value 1: yes
  

• oldpeak: ST depression induced by exercise relative to rest

• slope: the slope of the peak exercise ST segment

  * Value 1: upsloping
  * Value 2: flat
  * Value 3: downsloping
  

• ca: number of major vessels (0-3) colored by flourosopy

• thal:

  * Value 3: normal
  * Value 6: fixed defect
  * Value 7: reversable defect