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HCC_​Survival.knwf

HCC_Survival.knwf

HCC_Survival.knwf

This workflow reads in HCC.csv file and trains random forest, K-nearest and decision tree to classify patient that survive the diagnosed liver cancer. The results shows random forest excels in both accuracy as 0.75 and number of false positive predictions as 0.35.

DATA UNDERSTANDING DATA PREPARATION MODELING & EVALUATION - FINAL ALTERNATIVES Importing HCC Data setConvert nominal and ordinal values to stringsMissing values identifiedFinding corelationTreating outliersReplacing missing valuesComputing correlationsRemoving highly correlated attributesEvaluation of modelEvaluation of modelUsing Leave one out methodNormalizing attributesfor KNNUsing Leave one out methodOutliers detectedDealing with class imbalanceUsing Leave one out method 88Dealing with class imbalanceEvaluation of modelcoloring class attributescomparing performance of the model with dataDealing with class imbalance File Reader(deprecated) Number To String(deprecated) Statistics Scatter Plot Numeric Outliers Statistics Missing Value Linear Correlation(deprecated) Correlation Filter Scorer (deprecated) Random ForestLearner Random ForestPredictor Scorer (deprecated) X-Partitioner X-Aggregator X-Aggregator Normalizer X-Partitioner K Nearest Neighbor Box Plot (local) Statistics SMOTE X-Partitioner SMOTE DecisionTree Learner Decision TreePredictor X-Aggregator Scorer (deprecated) Forward FeatureSelection Color Manager InteractiveHistogram (local) SMOTE DATA UNDERSTANDING DATA PREPARATION MODELING & EVALUATION - FINAL ALTERNATIVES Importing HCC Data setConvert nominal and ordinal values to stringsMissing values identifiedFinding corelationTreating outliersReplacing missing valuesComputing correlationsRemoving highly correlated attributesEvaluation of modelEvaluation of modelUsing Leave one out methodNormalizing attributesfor KNNUsing Leave one out methodOutliers detectedDealing with class imbalanceUsing Leave one out method 88Dealing with class imbalanceEvaluation of modelcoloring class attributescomparing performance of the model with dataDealing with class imbalance File Reader(deprecated) Number To String(deprecated) Statistics Scatter Plot Numeric Outliers Statistics Missing Value Linear Correlation(deprecated) Correlation Filter Scorer (deprecated) Random ForestLearner Random ForestPredictor Scorer (deprecated) X-Partitioner X-Aggregator X-Aggregator Normalizer X-Partitioner K Nearest Neighbor Box Plot (local) Statistics SMOTE X-Partitioner SMOTE DecisionTree Learner Decision TreePredictor X-Aggregator Scorer (deprecated) Forward FeatureSelection Color Manager InteractiveHistogram (local) SMOTE

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: Chaitanya Upadrasta
Created at: 2019-06-04
On NodePit since: 2021-12-19
Last update: 2024-04-28
Created with KNIME version: v4.5.0

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