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
License: Apache License 2.0
Jupyter Notebook 100.00%
catboost_shap_smote's Introduction
๐ญ Iโm currently working on Hacettepe University Medical School Department of Pediatrics
๐ฑ Iโm currently learning Time Series
๐ฏ Iโm looking to collaborate on "Digital Health"
๐ฌ Ask me about Medicine with Artificial Intelligence
๐ซ How to reach me: