Heart-Disease-Diagnosis-Application

Heart Disease Prediction Pipeline using Luigi and Ensemble Modeling.

Check out the Streamlit Web Application below -

https://heart-disease-diagnostics.herokuapp.com/

Problem Definition

Given clinical parameters about a person, can we predict whether or not they have heart disease?

Dataset -

UCI Heart Disease dataset from Kaggle is being used for this project.

Features -

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 cholestoral 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)56

   • '>126' mg/dL signals diabetes

   

7. restecg - resting electrocardiographic results

   • 0: Nothing to note
   • 1: ST-T Wave abnormality
   • can range from mild symptoms to severe problems
   • signals non-normal heart beat
   • 2: Possible or definite left ventricular hypertrophy
   • Enlarged heart's main pumping chamber

8. thalach - maximum heart rate achieved

9. exang - exercise induced angina (1 = yes; 0 = no)

10. oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more

11. slope - the slope of the peak exercise ST segment

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

12. ca - number of major vessels (0-3) colored by flourosopy

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

13. thal - thalium stress result

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

14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)

feature Selection & Inference Pipeline (Luigi)

Based on the extensive feature selection using RFECV (Recursive feature elimination with Cross Validation) and Meta Transofrmer and looking at all aspects of Training accuracy, Testing Accuracy, Precision & Recall, I have selected 3 different models as the best estimators. I got a Test accuracy of 88.5% and f1 score of 89% on all of these.

I have further created a Ensemble max voting of the predictions from these 3 models to bring more generalization to the final prediction.

Max heart rate, Exercise induced Angina, Chest pain type and Major vessel counts seems to be most significant for Prediction of Heart Diseases.

I have developed a Inference pipeline too using luigi and deployed the Model as a service using Streamlit & Docker on Heroku.