Skip to content

Latest commit

 

History

History
109 lines (73 loc) · 4.88 KB

README.md

File metadata and controls

109 lines (73 loc) · 4.88 KB
Raw

Heart Disease Prediction using Machine Learning

Steps involved:

  • Exploratory data analysis (EDA) - the process of going through a dataset and finding out more about it.
  • Model training - create model(s) to learn to predict a target variable based on other variables.
  • Model evaluation - evaluating a models predictions using problem-specific evaluation metrics.
  • Model comparison - comparing several different models to find the best one.
  • Model fine-tuning - once we've found a good model, how can we improve it?
  • Feature importance - since we're predicting the presence of heart disease, are there some things which are more important for prediction?
  • Cross-validation - if we do build a good model, can we be sure it will work on unseen data?
  • Reporting what we've found - if we had to present our work, what would we show someone?

1. Problem Definition

In our case, the problem we will be exploring is binary classification (a sample can only be one of two things).

This is because we're going to be using a number of differnet features (pieces of information) about a person to predict whether they have heart disease or not.

In a statement,

Given clinical parameters about a patient, can we predict whether or not they have heart disease?

2. Data

What we'll want to do here is dive into the data our problem definition is based on. This may involve, sourcing, defining different parameters, talking to experts about it and finding out what we should expect.

The original data came from the Cleveland database from UCI Machine Learning Repository.

Howevever, we've downloaded it in a formatted way from Kaggle.

The original database contains 76 attributes, but here only 14 attributes will be used. Attributes (also called features) are the variables what we'll use to predict our target variable.

Attributes and features are also referred to as independent variables and a target variable can be referred to as a dependent variable.

We use the independent variables to predict our dependent variable.

Or in our case, the independent variables are a patients different medical attributes and the dependent variable is whether or not they have heart disease.

3. Evaluation

If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursure this project.

However, due to the nature of experimentation, the evaluation metric may change over time.

4. Features

Heart Disease Data Dictionary

The following are the features we'll use to predict our target variable (heart disease or no heart disease).

  1. age - age in years

  2. sex - (1 = male; 0 = female)

  3. cp - chest pain type

    • 0: Typical angina: chest pain related decrease blood supply to the heart
    • 1: Atypical angina: chest pain not related to heart
    • 2: Non-anginal pain: typically esophageal spasms (non heart related)
    • 3: Asymptomatic: chest pain not showing signs of disease

  4. trestbps - resting blood pressure (in mm Hg on admission to the hospital)

    • anything above 130-140 is typically cause for concern

  5. chol - serum cholestoral in mg/dl

    • serum = LDL + HDL + .2 * triglycerides
    • above 200 is cause for concern

  6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)

    • '>126' mg/dL signals diabetes

  7. restecg - resting electrocardiographic results

    • 0: Nothing to note
    • 1: ST-T Wave abnormality
    • can range from mild symptoms to severe problems
    • signals non-normal heart beat
    • 2: Possible or definite left ventricular hypertrophy
    • Enlarged heart's main pumping chamber

  8. thalach - maximum heart rate achieved

  9. exang - exercise induced angina (1 = yes; 0 = no)

  10. oldpeak - ST depression induced by exercise relative to rest

    • looks at stress of heart during excercise
    • unhealthy heart will stress more

  11. slope - the slope of the peak exercise ST segment

    • 0: Upsloping: better heart rate with excercise (uncommon)
    • 1: Flatsloping: minimal change (typical healthy heart)
    • 2: Downslopins: signs of unhealthy heart

  12. ca - number of major vessels (0-3) colored by flourosopy

    • colored vessel means the doctor can see the blood passing through
    • the more blood movement the better (no clots)

  13. thal - thalium stress result

    • 1,3: normal
    • 6: fixed defect: used to be defect but ok now
    • 7: reversable defect: no proper blood movement when excercising

  14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)

Note: No personal identifiable information (PPI) can be found in the dataset.

Libraries Required

  • pandas for data analysis.
  • NumPy for numerical operations.
  • Matplotlib/seaborn for plotting or data visualization.
  • Scikit-Learn for machine learning modelling and evaluation.