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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')
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