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Using deep learning and random forest classification to predict survival after thoracic surgery

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Classifying thoracic surgery survival using a quick random forest classifier baseline, deep learning, and SMOTE oversampling to compensate for class imbalance

Data used is from UCI Machine Learning Repository

First decision tree visualization

Tree

Model learning curves

Accuracy

Loss

Simple dense model

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Using deep learning and random forest classification to predict survival after thoracic surgery

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