/Heart-Disease-AI

This dataset has been trained using a decision tree structure model. Necessary hyperparameters and features have been used to prevent overfitting.

Primary LanguageJupyter Notebook

Heart Disease AI

Description

This dataset has been trained using a decision tree structure model. Necessary hyperparameters and features have been used to prevent overfitting.

Context

This data set dates from 1988 and consists of four databases: Cleveland, Hungary, Switzerland, and Long Beach V. It contains 76 attributes, including the predicted attribute, but all published experiments refer to using a subset of 14 of them. The "target" field refers to the presence of heart disease in the patient. It is integer valued 0 = no disease and 1 = disease.

Content

  1. age.
  2. gender.
  3. chest pain type (4 values).
  4. resting blood pressure.
  5. serum cholestoral in mg/dl.
  6. fasting blood sugar > 120 mg/dl.
  7. resting electrocardiographic results (values 0,1,2).
  8. maximum heart rate achieved.
  9. exercise induced angina.
  10. oldpeak = ST depression induced by exercise relative to rest.
  11. the slope of the peak exercise ST segment.
  12. number of major vessels (0-3) colored by flourosopy.
  13. thal: 0 = normal; 1 = fixed defect; 2 = reversable defect.

The names and social security numbers of the patients were recently removed from the database, replaced with dummy values.