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Arman Ulusoy 2024-06-22 23:14:25 +02:00
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@ -218,11 +218,12 @@ The selection of features was informed by an analysis presented in a paper (sour
The exact process can be found in the notebook: [features_detection.ipynb](notebooks/features_detection.ipynb).
### ML-models
For machine learning, the initial step involved tailoring the features for the models, followed by employing a grid search to identify the best hyperparameters. This approach led to the highest performance being achieved by the Extreme Gradient Boosting (XGBoost) model, which attained an accuracy of 83%. Additionally, a Gradient Boosting Tree model was evaluated using the same procedure and achieved an accuracy of 82%. The selection of these models was influenced by the team's own experience and the performance metrics highlighted in the paper (source: https://rdcu.be/dH2jI, last accessed: 15.05.2024). The models have also been evaluated, and it is noticeable that some features, like the ventricular rate, are shown to be more important than other features.
For machine learning, the initial step involved tailoring the features for the models, followed by employing a grid search to identify the best hyperparameters. This approach led to the highest performance being achieved by the Extreme Gradient Boosting (XGBoost) model, which attained an accuracy of 83%. Additionally, a Gradient Boosting Tree model was evaluated using the same procedure and achieved an accuracy of 82%. A Decision Tree model was also evaluated, having the lowest performance of 80%. The selection of these models was influenced by the team's own experience and the performance metrics highlighted in the paper (source: https://rdcu.be/dH2jI, last accessed: 15.05.2024). The models have also been evaluated, and it is noticeable that some features, like the ventricular rate, are shown to be more important than other features.
<br>The detailed procedures can be found in the following notebooks:
<br>[ml_xgboost.ipynb](notebooks/ml_xgboost.ipynb)
<br>[ml_grad_boost_tree.ipynb](notebooks/ml_grad_boost_tree.ipynb)
<br>[ml_decision_tree.ipynb](notebooks/ml_decision_tree.ipynb)
## Contributing