MLZoomcamp 2022 Midterm project

Heart Failure Prediction

Description:

Cardiovascular problems are the number 1 cause of death globally, the majority of fatal cardiovascular diseases are heart attacks and strokes and some of this health problems can occurs in individuals under 70 years old. Heart failure is a common event caused by cardiovascular diseases and can be predicted using some patient information. Then, classification machine learning models could be used to help doctors to detect this kind of event in patients with cardiovascular diseases or with high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease).

In order to do this task, this project offers a classification model trained using the Heart Failure Prediction Dataset, where it has 11 columns with medical and personal information that can be used to predict the probability of a heart failure in a given patient.

How to run the application:

1. Docker

Step 1: Clone the GitHub repository with the project.

git clone https://github.com/jvscursulim/mlzoom2022_midterm_project

Step 2: Access the GitHub repository folder.

cd mlzoom2022_midterm_project

Step 3: Create the docker image.

docker build -t heart_failure_prediction .

Step 4: Run the application with docker.

docker run -p 4242:4242 heart_failure_prediction

2. Without Docker

Step 1: Clone the GitHub repository with the project.

git clone https://github.com/jvscursulim/mlzoom2022_midterm_project

Step 2: Access the GitHub repository folder.

cd mlzoom2022_midterm_project

Step 3: Create a virtual environment.

python -m venv env

Step 4: Activate your virtual environment.

• Linux: Activation of the virtual environment.

source env/bin/activate

• Windows: Activation of the virtual environment.

env/Scripts/Activate.ps1

Step 5: Install pipenv.

pip install pipenv

Step 6: Install the packages required for this application using the command below.

pipenv install

Step 7: Run the application with gunicorn.

gunicorn --bind=0.0.0.0:4242 predict:app

Observation: If you want to train a model

Access script folder.

cd script

Make your changes in train.py and run the file using the command below.

python train.py

After training process, you can build the new application following the instructions in the sections 1. Docker and 2. Without Docker.

How to send data for the application:

Code snippet

import requests

patient = {'age': 47.0,
           'sex': 0.0,
           'chest_pain_type': 1.0,
           'resting_bp': 130.0,
           'cholesterol': 235.0,
           'fasting_bs': 0.0,
           'resting_ecg': 0.0,
           'max_hr': 145.0,
           'exercise_angina': 0.0,
           'oldpeak': 2.0,
           'st_slope': 0.0}

url = "http://localhost:4242/predict"
print(requests.post(url, json=patient).json())

References:

[1] Alexey Grigorev, "Machine Learning Zoomcamp". https://github.com/alexeygrigorev/mlbookcamp-code/tree/master/course-zoomcamp

[2] fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/heart-failure-prediction.