Model Performance Results For Distribution-Driven Augmentation of Medical Data

The data included here within is the associated model training results from the correlated paper Distribution-Driven Augmentation of Real-World Datasets for Improved Cancer Diagnostics With Machine Learning. This paper focuses on using kernel density estimators to curate datasets by balancing classes and filling missing null values though synthetically generated data. Additionally, this manuscript proposes a technique for joining distinct datasets to train a model with necessary features from multiple different datasets as a type of transfer-learning. The specific data provided here is the performance results of each model in question (Naive Bayes, Logistic Regression, Support Vector Machine, Decision Tree, and a Voting Classifier) using 5-Fold Cross Validation. In particular, these models were evaluated using DDA, our novel solution, compared against other frequently used techniques.