heart-disease

Predicting Heart Disease using SHAInet

Predicting Heart Disease using SHAInet

This workbook predicts the probability of heart disease. We are using SHAInet modeling tool in Crystal.

This is for research purposes only and should not be used to diagnose or predict any actual persons health.

Data

We will be using the Heart Disease Dataset provided on kaggle.com by Nikhil Anand.

Heart Disease Data Set: Features:

  1. #3 (age)
  2. #4 (sex) (0 = female, 1 = male)
  3. #9 (cp) cp: chest pain type -- 1: typical angina -- 2: atypical angina -- 3: non-anginal pain -- 4: asymptomatic
  4. #10 (trestbps) trestbps: resting blood pressure (in mm Hg on admission to the hospital)
  5. #12 (chol) chol: serum cholesterol in mg/dl
  6. #16 (fbs) fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
  7. #19 (restecg) 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
  8. #32 (thalach) halach: maximum heart rate achieved
  9. #38 (exang) exercise induced angina (1 = yes; 0 = no)
  10. #40 (oldpeak) oldpeak = ST depression induced by exercise relative to rest
  11. #41 (slope) slope: the slope of the peak exercise ST segment -- Value 1: upsloping -- Value 2: flat -- Value 3: downsloping
  12. #44 (ca) ca: number of major vessels (0-3) colored by flourosopy
  13. #51 (thal) thaldur: duration of exercise test in minutes
  14. #58 (num) (the predicted attribute) num: diagnosis of heart disease (angiographic disease status) -- Value 0: < 50% diameter narrowing -- Value 1: > 50% diameter narrowing (in any major vessel: attributes 59 through 68 are vessels)

Creators:

  1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
  2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
  3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
  4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

Installation

This requires crystal 0.24.2 to be installed

Usage

This project uses crystal's playground. You can load and run the playground workbook using:

shards install
crystal play
open http://localhost:8080

Then select the Workbook -> Heart Disease from the menu.

You can also compile and run the application:

crystal run src/heart_disease.cr

Contributing

  1. Fork it ( https://github.com/drujensen/heart-disease/fork )
  2. Create your feature branch (git checkout -b my-new-feature)
  3. Commit your changes (git commit -am 'Add some feature')
  4. Push to the branch (git push origin my-new-feature)
  5. Create a new Pull Request

Contributors

Owner
github statistic
  • 10
  • 0
  • 12
  • 0
  • over 2 years ago
  • May 4, 2018
License

MIT License

Links
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Fri, 15 Jan 2021 20:25:23 GMT