Introduction to data mining (especially for healthcare domain)

on YouTube ↗01h 23m

" summary

Hands-on demo using the Orange GUI to build a binary heart‑disease classifier on a cleaned dataset reduced to 14 attributes (originally >76). Data come from the UCI‑style heart file of about 3002 patients and the target is binarized to 0/1. Workflow shows import, edit domain, simple preprocessing, a 70/30 train/test split, decision tree (train precision ≈0.94, similar on test) and a neural network (~87%); confusion matrices and overfitting issues are visualized.

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" chapters (19)

1. 0:00Data mining tutorial
2. 2:15Tutorial goals
3. 3:21Why Orange
4. 5:54Heart disease prediction
5. 8:05Dataset attributes
6. 14:19Orange tool
7. 18:36Purpose of data mining
8. 20:24Data mining steps
9. 21:51Importing datasets
10. 27:29Data preprocessing
11. 31:09Target attribute for classification
12. 38:39Applying data mining
13. 40:03Supervised learning overview
14. 47:02Decision tree classifier
15. 56:18Model performance
16. 1:01:33Confusion matrix
17. 1:11:33Visualizing decision tree
18. 1:14:31Neural networks
19. 1:17:07Resources and next steps

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