Merge branch 'main' of https://gitty.informatik.hs-mannheim.de/1826514/DSA_SS24

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
@@ -243,10 +244,20 @@ Please note, by contributing to this project, you agree that your contributions
 
 We look forward to your contributions. Thank you for helping us improve this project!
 
-
 # Update from 03.07
 
+## What was expanded?
+- In addition to the Gradient Tree and Extreme Gradient Boosting models, the Decision Tree model was used, which is explained in more detail in the ["ML models"](#ml-models) section
+- Grafik Nils?
+
+## What new insights could be gained?
+- 
+- 
+
 ## Conclusion
+- Machine learning and data analysis as valuable tools for investigating cardiovascular diseases 
+
+ - Improvement of diagnostics and treatment possible through predictive modeling
 
 ## Outlook into the future
 

notebooks/ml_decision_tree.ipynb

@@ -40,7 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Connect to the database\n",
+    "\n",
     "conn = sqlite3.connect('../features.db')\n",
     "c = conn.cursor()\n",
     "\n",