diff --git a/README.md b/README.md
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+++ b/README.md
@@ -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