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   - Female ratio: 42.66%
 This indicates a potential demographic bias towards older age groups and a gender imbalance.
 
-# TODO
-- Zustimmung und Anonymität: 
-- Datenschutz und Ethik: 
+## Data protection and ethics
+(version 03.07)
 
+The data used in the project was approved by the review boards of Shaoxing People's Hospital and Ningbo First Hospital of Zhejiang University. Both institutions allowed public disclosure of the data after de-identification. While Shaoxing People's Hospital additionally waived the informed consent requirement, Ningbo First Hospital also did not require patient consent.
 
 ## Conclusion 
 (version 03.07)
 
-- Machine learning and data analysis as valuable tools for investigating cardiovascular diseases 
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-- Improvement of diagnostics and treatment possible through predictive modeling
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-This project impressively demonstrated the feasibility and benefits of applying modern data analysis methods and machine learning in the field of cardiology. By using a large dataset of 12-lead ECGs, our team was able to effectively classify different cardiac arrhythmias using models such as XGBoost, gradient boosting and decision trees. These models achieved a classification accuracy of over 80%, highlighting the importance of accurate diagnostic tools.
+This project has impressively demonstrated the feasibility and benefits of applying modern data analysis methods and machine learning in the field of cardiology. By using a large dataset of 12-lead ECGs, it was possible to effectively classify different cardiac arrhythmias using models such as XGBoost, gradient boosting and decision trees. These models achieved classification accuracies of and above 80%, highlighting the importance of accurate diagnostic tools.
 
 Despite these successes, we encountered challenges such as the lack of datasets for certain demographic groups and the handling of incomplete ECG recordings. These limitations emphasize the need for further research to improve data collection and processing in medical studies.
 
-The application of these analysis techniques not only offers the possibility of making diagnoses more accurately and quickly, but also opens up avenues for the development of personalized treatment approaches tailored to specific patient-individual data. The results of our project suggest that the integration of data science and AI into clinical practice has the potential to significantly improve the treatment of cardiovascular disease.
+The application of these analytical techniques not only provides the opportunity to make more accurate and faster diagnoses, but also opens avenues for the development of personalized treatment approaches tailored to specific patient-individual data. 
+
+Ultimately, our research shows that the continued integration and improvement of technological solutions in medical diagnostic procedures is essential for future healthcare. We recommend continuing research in this direction.
 
-Ultimately, our research shows that the continued integration and improvement of technological solutions into medical diagnostic processes is essential for future healthcare. We recommend continued research in this direction to further increase diagnostic accuracy while ensuring the ethical aspects of data use.
 
-By understanding the possibilities and limitations of our applied methods, we are confident that the way is paved for future innovations in medical diagnostics. This will ultimately help to improve the quality of life of patients worldwide.
 
 ## Outlook
 (version 03.07)
 
-Other models can be used and improved in the future. Other features can also be used.
-- Use of deep learning
-- Expansion of the database:
+As data science advances, there are several opportunities to improve and expand current research on analyzing cardiovascular disease using ECG data. 
+Key future directions include:
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+- Advanced machine learning techniques: Incorporating more machine learning methods such as deep learning could improve the accuracy and reliability of cardiovascular disease diagnosis. 
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+- Expansion of features used: Future developments could include expanding the feature sets used. By adding new predictive features or enhancing existing features, the precision of the models could be further improved.
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+- Data augmentation: Data augmentation techniques could be used to solve problems such as unbalanced datasets or underrepresented classes. This would help to create a more robust model by generating synthetic ECG data that provides the machine learning models with more examples to learn from.
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+- Integration of additional data sources: Expanding the database to include more diverse datasets from different geographic and demographic contexts could help mitigate local and demographic biases. 
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