Heart disease prediction model made with sci-kit learn.

Setup your virtual environment and run the following command:

python app.py

{
    "features":[[57,0,0,140,241,0,1,123,1,0.2,1,0,3]]
}

Once the app is up and running the hit this endpoint with above payload: http://localhost:5000/predict

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 cholesterol 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
   • 2: Possible or definite left ventricular hypertrophy, Enlarged heart's main pumping

8. thalach - maximum heart rate achieved,

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

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

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

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

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