Heart disease#
Objective#
Training a model for the diagnosis of coronary artery disease (binary classification).
Context#
The dataset is provided by the Cleveland Clinic Foundation for Heart Disease (more information). The dataset file to use is available here. Each row describes a patient. Below is a description of each column.
Column |
Description |
Feature Type |
Data Type |
|---|---|---|---|
Age |
Age in years |
Numerical |
integer |
Sex |
(1 = male; 0 = female) |
Categorical |
integer |
CP |
Chest pain type (0, 1, 2, 3, 4) |
Categorical |
integer |
Trestbpd |
Resting blood pressure (in mm Hg on admission to the hospital) |
Numerical |
integer |
Chol |
Serum cholestoral in mg/dl |
Numerical |
integer |
FBS |
(fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) |
Categorical |
integer |
RestECG |
Resting electrocardiographic results (0, 1, 2) |
Categorical |
integer |
Thalach |
Maximum heart rate achieved |
Numerical |
integer |
Exang |
Exercise induced angina (1 = yes; 0 = no) |
Categorical |
integer |
Oldpeak |
ST depression induced by exercise relative to rest |
Numerical |
float |
Slope |
The slope of the peak exercise ST segment |
Numerical |
integer |
CA |
Number of major vessels (0-3) colored by flourosopy |
Numerical |
integer |
Thal |
3 = normal; 6 = fixed defect; 7 = reversable defect |
Categorical |
string |
Target |
Diagnosis of heart disease (1 = true; 0 = false) |
Classification |
integer |
Instructions and advice#
Follow the main steps of a supervised ML project: data loading and exploring, data preparation, model training and evaluation.
Use the scikit-learn library for data preparation and model training. If you are new to it, consider following its Getting started guide.
Don’t forget to setup your environment by importing the necessary Python packages.
Data preparation should be very similar to the regression example.
You may train any binary classification model, for example a simple SGDClassifier.
Model evaluation should be very similar to the classification example.
Assess model performance and interpret results on test data.
Bonus: train several other models (decision tree, artificial neural network, etc) and compare their performances.