update

README.md

@@ -163,6 +163,7 @@ The following two hypotheses were applied in this project:
     Result: 
 - For the first hypothesis, an accuracy of 83 % was achieved with the XGBoost classifier. The detailed procedure can be found in the following notebook: [ml_xgboost.ipynb](notebooks/ml_xgboost.ipynb)
 - Also a 82 % accuracy was achieved with a Gradient Boosting Tree Classifier. The detailed procedure can be found in the following notebook: [ml_grad_boost_tree.ipynb](notebooks/ml_grad_boost_tree.ipynb)
+- An 80 % accuracy was achieved with a Decision Tree Classifier. The detailed procedure can be found in the following notebook: [ml_decision_tree.ipynb](notebooks/ml_decision_tree.ipynb)
 
 With those Classifiers, the hypothesis can be proven, that a classifier is able to classify the diagnostic Groups with a accuracy of at least 80%.