University of Stirling

Machine Learning Driven Heart Disease Prognostic Model



1. Age: Years

2. Sex:

3. Chest Pain Type:

4. Exercise Induced Angina:

5. Resting BP (in mm Hg on admission to the hospital):

6. Serum Cholesterol in mmol/L:(If Unknown leave this field empty)

7. (Fasting Blood Sugar > 120 mg/dl) ?

8. Resting Electrocardiography Results
  1. 0: Normal
  2. 1: Having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)
  3. 2: Showing probable or definite left ventricular hypertrophy by Estes' criteria


9. ST Depression Induced by Exercise Relative to Rest :

10. ST Segment:

11. Number of Major Vessels Coloured by Fluoroscopy:

12. Thallium Treadmill Stress Test: Maximum Heart Rate Achieved:

13. Thallium Heart Scan:


Developed by Kamran Farooq, Hicham Atassi, Thomas Mazzocco, Stephen Leslie1, Calum MacRae2, Chris Eckl3,Warner Slack4, Amir Hussain.
This research project is funded by the EPSRC (Grant Ref. No. EP/H501584/1) and 3Sitekit Solutions Ltd in collaboration with 1Cardiology Clinic, Raigmore Hospital 2Brigham and Women's Hospital, Harvard Medical School and 4Beth Israel Deaconess Medical Centre, Harvard Medical School.
All rights reserved. Pilot prototype provided "as is" without any warranty.