Cardiovascular-Disease-Detection

Updating Your Code to Include a README.md

You can modify your upload code to create a README.md file along with the model:

Model Overview

This model predicts the presence of cardiovascular disease based on various health metrics.

Cardiovascular Disease Detection Model

Model Overview

This model predicts the presence of cardiovascular disease based on various health metrics.

Dataset

The model was trained on the Cardiovascular Disease dataset.

Performance Metrics

Classification Report

Accuracy: 0.7368571428571429

Accuracy of Keras model: 0.5844

      -       precision    recall  f1-score   support

     0          0.81      0.23      0.36      6988
     1          0.55      0.94      0.70      7012
 accuracy                           0.59      14000
 macro avg      0.68      0.59      0.53      14000
 weighted avg   0.68      0.59      0.53      14000

training and evaluation data

Full info in .ipynb file

Getting Feature Importances

Here’s how you can train a GradientBoostingClassifier, extract feature importances

      |id            | 0.0025 |
      |age           | 0.1255 |
      |gender        | 0.0004 |
      |height        | 0.0025 |
      |weight        | 0.0167 |
      |ap_hi         | 0.7385 |
      |ap_lo         | 0.0246 |
      |cholesterol   | 0.0756 |
      |gluc          | 0.0043 |
      |smoke         | 0.0034 |
      |alco          | 0.0020 |
      |active        | 0.0041 |

      

Training and evaluation data

Full info in .py file

Usage

import tensorflow as tf

#Load the model
model = tf.keras.models.load_model('apipyo/Cardiovascular-Disease-Detection')