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Nils 2024-06-30 17:00:12 +02:00
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@ -266,7 +266,7 @@ Further analysis included the creation of a Euclidean distance matrix plot to vi
Finally, a parallel axis plot was generated to examine the relationship between data features and the clusters. Notably, this plot highlighted the ventricular rate feature as a significant separator in the original labels, underscoring its importance as identified by our machine learning models in predicting the labels. Finally, a parallel axis plot was generated to examine the relationship between data features and the clusters. Notably, this plot highlighted the ventricular rate feature as a significant separator in the original labels, underscoring its importance as identified by our machine learning models in predicting the labels.
![Alt-Text](readme_data/Cluster_analysis.png) ![Alt-Text](readme_data/cluster_analysis.png)
<br>The detailed procedures can be found in the following notebook: <br>The detailed procedures can be found in the following notebook:
@ -318,6 +318,7 @@ Key future directions include:
- 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. - 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.
- Visual Data comparsion: With the use of the given ECG data there could be calculated a "standardized" QRS-Complex for every Diagnosisgroup which were examined in this program to classify new unknown ECG data with no prior diagnosis.