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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.

Glucose levels exceeding 130 coupled witha BMI of >30 signficantly increases the riskof diabetes. Sample run for Logistic Learner to comparethe accuracy outcome of the AutoMLcomponent versus the most probablesimplistic Stats Model Node 1Node 2Node 5execute up-streambefore configurationNode 7Node 8 Input:0 : Model as a Workflow Object1 : Data from Model Test PartitionOutput:0 : Global Feature ImportanceNode 11Glucose vs BMIScatter PlotNode 13Node 14Node 15CSV Reader Partitioning Workflow Executor AutoML Number To String Scorer Global FeatureImportance Color Manager Scatter Plot(Plotly) LogisticRegression Learner Logistic RegressionPredictor Scorer Glucose levels exceeding 130 coupled witha BMI of >30 signficantly increases the riskof diabetes. Sample run for Logistic Learner to comparethe accuracy outcome of the AutoMLcomponent versus the most probablesimplistic Stats Model Node 1Node 2Node 5execute up-streambefore configurationNode 7Node 8Input:0 : Model as a Workflow Object1 : Data from Model Test PartitionOutput:0 : Global Feature ImportanceNode 11Glucose vs BMIScatter PlotNode 13Node 14Node 15CSV Reader Partitioning Workflow Executor AutoML Number To String Scorer Global FeatureImportance Color Manager Scatter Plot(Plotly) LogisticRegression Learner Logistic RegressionPredictor Scorer

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