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@ -251,6 +251,8 @@ For machine learning, the initial step involved tailoring the features for the m
- The data used all come from one hospital - 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 - Most of the data are from people of older age, predominantly from the 60-70 age group
- Zustimmung und Anonymität:
- Datenschutz und Ethik:
## Conclusion ## Conclusion
@ -260,15 +262,28 @@ For machine learning, the initial step involved tailoring the features for the m
- Improvement of diagnostics and treatment possible through predictive modeling - Improvement of diagnostics and treatment possible through predictive modeling
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.
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.
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 ## Outlook
(version 03.07) (version 03.07)
Other models can be used and improved in the future. Other features can also be used. Other models can be used and improved in the future. Other features can also be used.
- Use of deep learning
- Expansion of the database:
## Contributing ## Contributing
(version 12.06) (version 12.06)
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: 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. - **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.