This code defines a Flask app that receives user input about cardiovascular disease risk factors, predicts whether the user has a high or low likelihood of having cardiovascular disease, and returns the prediction along with a plot showing the feature importance for the prediction. The app loads a dataset of cardiovascular disease risk factors, trains a random forest classifier on the data, and calculates the model's accuracy. When a user submits their data, the app preprocesses the data, makes a prediction using the trained model, calculates the accuracy of the prediction, and creates a plot of the feature importance. Finally, the app returns the prediction, plot, and accuracy to the user.
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feature importances from the trained clf classifier.
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Sorting feature importances in descending order using
np.argsort()and reverse the order using[::-1] -
Reorder the user data according to the sorted feature importances.
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plot graph
- Apart from prediction, accuracy scores and graphical metrics
- Natural language input is also acceptable but within the given parameters
- The model, then accurately returns in natural language an accurate prediction of cvd likelihood
- Objective: factual information;
- Examination: results of medical examination;
- Subjective: information given by the patient.
Features:
- Age | Objective Feature | age | int (days) ✓
- Height | Objective Feature | height | int (cm) | ✓
- Weight | Objective Feature | weight | float (kg) | ✓
- Gender | Objective Feature | gender | categorical code | ✓
- Systolic blood pressure | Examination Feature | ap_hi | int | ✓
- Diastolic blood pressure | Examination Feature | ap_lo | int | ✓
- Cholesterol | Examination Feature | cholesterol | 1: normal, 2: above normal, 3: well above normal | ✓
- Glucose | Examination Feature | gluc | 1: normal, 2: above normal, 3: well above normal | ✓
- Smoking | Subjective Feature | smoke | binary | ✓
- Alcohol intake | Subjective Feature | alco | binary | ✓
- Physical activity | Subjective Feature | active | binary | ✓
- Presence or absence of cardiovascular disease | Target Variable | cardio | binary | ✓
All of the dataset values were collected at the moment of medical examination.
The dataset consists of 70 000 records of patients data, 11 features + target.
- Pre-requisites (Python3, pip, vscode(or other IDE))
- run
pip installto install necessary packages - run
python -m venv envto create a virtual envirnment - activate the environmnet through
projectfolder\env\Scripts\Activate - run
python app.py, the app will open on port5000 or any free localhost port - The production version is found at Heroku