update

README.md

@@ -16,6 +16,7 @@ This project was developed through the Data Science and Analytics course at the
 
 
 ## About 
+(version 12.06)
 
 Cardiovascular diseases refer to a group of diseases that affect the heart and blood vessels and represent a significant global health burden. They are a leading cause of morbidity and mortality worldwide, making effective prevention and management of these diseases critical. Physical examinations, blood tests, ECGs, stress or exercise tests, echocardiograms and CT or MRI scans are used to diagnose cardiovascular disease.
 (source: https://www.netdoktor.de/krankheiten/herzkrankheiten/, last visit: 15.05.2024)
@@ -41,13 +42,16 @@ The data provision provides for the following points, which can be taken from th
 
 
 ## Getting Started 
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 This project is implemented in Python. Follow these steps to set up and use the project:
 
 ### Prerequisites 
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 - Ensure you have Python 3.8 or newer installed on your system.
 - At least `10 GB` of available disk space and `32 GB` of RAM are recommended for optimal performance.
 
 ### Installation 
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 1. **Download the Dataset:**
    - Visit [the dataset page](https://doi.org/10.13026/wgex-er52) (last visited: 15.05.2024) and download the dataset.
    - Extract the dataset to a known directory on your system.
@@ -61,6 +65,7 @@ This project is implemented in Python. Follow these steps to set up and use the
    - Adjust the parameters as needed, especially the path variables to match where you extracted the dataset.
 
 ### Generating Data 
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 1. **Generate Basic Data Files:**
    - In the terminal, ensure you are in the project directory.
    - Run `generate_data.py` `main-function` with the folloing parameters `gen_data=True` `gen_features=False` to generate several pickle files. This process may take some time.
@@ -115,9 +120,12 @@ Through this process, Emma was able to leverage our project to generate meaningf
 - **[Noise reduction](#noise-reduction)**
 - **[Features](#features)**
 - **[ML-models](#ml-models)**
+- **[Cluster analysis](#cluster-analysis)**
+- **[Legal basis](#legal-basis)
 
 
 ### Data cleaning 
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 The following criteria were checked to ensure data quality:
 
@@ -127,6 +135,7 @@ The following criteria were checked to ensure data quality:
 - Number of data records that could not be read in
 
 ### Demographic plots 
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 #### Histogram
 The following histogram shows the age distribution. It illustrates the breakdown of the grouped diagnoses by age group as well as the absolute frequencies of the diagnoses.
@@ -161,9 +170,9 @@ The following two hypotheses were applied in this project:
 1. Using ECG data, a classifier can classify the four diagnostic groupings with an accuracy of at least 80%.
 
     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)
+- 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) (version 12.06)
-- 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)
+- 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) (version 12.06)
-- 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)
+- 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) (version 03.07)
 
 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%.
 
@@ -172,7 +181,7 @@ With those Classifiers, the hypothesis can be proven, that a classifier is able
 2. Sinus bradycardia occurs significantly more frequently in the 60 to 70 age group than in other age groups.
 
 
-    The second hypothesis was tested for significance using the chi-square test. The detailed procedure can be found in the following notebook: [statistics.ipynb](notebooks/statistics.ipynb)
+    The second hypothesis was tested for significance using the chi-square test. The detailed procedure can be found in the following notebook: [statistics.ipynb](notebooks/statistics.ipynb) (version 12.06)
 
     Result:
 
@@ -195,11 +204,13 @@ With those Classifiers, the hypothesis can be proven, that a classifier is able
     The sample size in the study conducted may also play a role in the significance of the frequency.
 
 ### Noise reduction 
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 Noise suppression was performed on the existing ECG data. A three-stage noise reduction was performed to reduce the noise in the ECG signals. First, a Butterworth filter was applied to the signals to remove the high frequency noise. Then a Loess filter was applied to the signals to remove the low frequency noise. Finally, a non-local-means filter was applied to the signals to remove the remaining noise. For noise reduction, the built-in noise reduction function from NeuroKit2 `ecg_clean` was utilized for all data due to considerations of time performance.
 
 How the noise reduction was performed in detail can be seen in the following notebook: [noise_reduction.ipynb](notebooks/noise_reduction.ipynb)
 
 ### Features 
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 The detection ability of the NeuroKit2 library is tested to detect features in the ECG dataset. Those features are important for the training of the model in order to detect the different diagnostic groups. The features are detected using the NeuroKit2 library.
 
 For the training, the features considered are: 
@@ -222,12 +233,37 @@ The exact process can be found in the notebook: [features_detection.ipynb](noteb
 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_xgboost.ipynb](notebooks/ml_xgboost.ipynb)(version 12.06)
-<br>[ml_grad_boost_tree.ipynb](notebooks/ml_grad_boost_tree.ipynb)
+<br>[ml_grad_boost_tree.ipynb](notebooks/ml_grad_boost_tree.ipynb) (version 12.06)
-<br>[ml_decision_tree.ipynb](notebooks/ml_decision_tree.ipynb)
+<br>[ml_decision_tree.ipynb](notebooks/ml_decision_tree.ipynb) (version 03.07)
+
+
+### Cluster-analysis
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+[](notebooks/...)
+
+
+## Legal basis 
+(version 03.07)
+- The data used all come from one hospital
+- Most of the data are from people of older age, predominantly from the 60-70 age group
+
+
+## Conclusion 
+(03.07)
+- 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 
+(03.07)
+-  In der Zukunft weitere Modelle anwenden und verbessern
+- 
+-
 
 
 ## Contributing 
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 Thank you for your interest in contributing to our project! As an open-source project, we welcome contributions from everyone. Here are some ways you can contribute:
 
 - **Reporting Bugs:** If you find a bug, please open an issue on our GitHub page with a detailed description of the bug, steps to reproduce it, and any other relevant information that could help us fix it.
@@ -246,26 +282,6 @@ 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!
 
 
-## Legal basis (03.07)
-- The data used all come from one hospital
-- Most of the data are from people of older age, predominantly from the 60-70 age group
-
-## What was expanded? (03.07)
-- 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
-- Cluster analysis
-- Grafik Nils?
-
-
-## Conclusion (03.07)
-- 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 (03.07)
--  In der Zukunft weitere Modelle anwenden und verbessern
-- 
--
-
 
 ## License
 This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).