heart-disease-diagnosis-ML-models

Machine Learning models to predict heart disease

A python program which used data analysis techniques to observe trends between various risk factors for heart diseases. Then, machine leaning models were created to predict whether a person has heart disease based on those features.

Required packages

1. Requires Python 3.7 or above.
2. Requires these modules:
   • NumPy
   • pandas
   • matplotlib
   • tkinter
   • scikit-learn
   • joblib

Running the program

1. Download the whole repository
2. extract the code
3. open heart_CAD.py and press enter to run the program

Note: I used Spyder IDE along with anaconda throughout this project (on Windows 10)

Models used:

1. Logistic Regression
2. Decision Trees
3. K-nearest neighbors

Desktop interface

General information

The Cleveland dataset (processed.cleveland.data) that can be downloaded from the UCI Machine Learning Repository via the following website: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

1. Attribute Information
   • (age) : Integer: [29-77]
   • #4 (sex)
   • #9 (cp)
   • #10 (trestbps) : Integer: [94-200]
   • #12 (chol) : Integer: [126-564]
   • #16 (fbs)
   • #19 (restecg)
   • #32 (thalach) : Integer: [71-202]
   • #38 (exang)
   • #40 (oldpeak) : Float: [0.0-62.0]
   • #41 (slope)
   • #44 (ca)
   • #51 (thal)
   • num: diagnosis of heart disease (angiographic disease status) : The target labels spanned from 0 to 4, with 0 indicating least chances of having a heart disease whereas label 4, indicating highest chances of having a heart disease

Contributing

Pull requests are welcome for adding more ML models or fixing exisiting issues.