Diabetes_Exercise
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As part of the JKI Challenge 19 - Dealing with Diabetes, the Auto ML component was utilized to determine the optimal model for classifying patients as either diabetic or non-diabetic. Following this, the Global Feature Importance component was employed to identify the two most crucial features that contribute to a diagnosis of "Diabetic" or an Outcome value of 1 in the model. Finally, a Scatter Plot was generated using complete data to explore how these two key components interact and ultimately indicate an elevated risk for diabetes.
Nodes
Extensions
- FeatureKNIME Base nodes
- FeatureKNIME Data Generation
- FeatureKNIME Deep Learning - Keras Integration
- FeatureKNIME Ensemble Learning Wrappers
- FeatureKNIME H2O Machine Learning Integration
- FeatureKNIME Integrated Deployment
- FeatureKNIME JavaScript Views
- FeatureKNIME Javasnippet
- FeatureKNIME Machine Learning Interpretability Extension
- FeatureKNIME Math Expression (JEP)
- FeatureKNIME Optimization extension
- FeatureKNIME PMML Preprocessing Applier Nodes
- FeatureKNIME Quick Forms
- FeatureKNIME XGBoost Integration
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