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PK Score
15854
12540481

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open Graph Image
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open Graph Image
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is In Organization
No
is In Organization
Yes
description
Of all the applications of artificial intelligence, diagnosing any disease using a "black box" is always going to be a hard explanation. Those who will use the application will want to know how the model decides on the treatment conditions or following-up conditions according to the model result. Or data provider clinicians will want the model with the highest performance in their project. This dataset classified patients according to sacral position properties. I investigated using the below techniques for the best result and explainable machine learning model; Balancing unbalanced medical data Creating models with CatBoost Classifier Finding the most optimized parameters by Grid Search with the Optuna library Artificial intelligence algorithms described as Black Box are actually explainable SHAP library tutorial Combined use of RFECV and SHAP library for Feature Selection Comparison of all applied models to each other
description
The library for web and native user interfaces.
homepage
homepage
https://react.dev
url
https://githubhelp/turkalpmd/catboost_shap_smote
url
https://githubhelp/facebook/react
disk Usage
1640
dis kUsage
523341
stargazer Count
1
stargazer Count
224887
forkCount
0
forkCount
45861
watcher Count
1
watcher Count
224887
created At
2022-07-27
created At
2013-05-25
license Info
Apache License 2.0
license Info
MIT License
issues
0
issues
50
languages
1
languages
12
repository Topics
0
repository Topics
6
owner
turkalpmd
owner
facebook
owner Avatar
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owner Avatar
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other
other

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