A Rebalancing Framework for Classification of Imbalanced Medical Appointment No-show Data

Abstract
Purpose: This paper aims to improve the classification performance when the data is
imbalanced by applying different sampling techniques available in Machine Learning.
Design/methodology/approach: The medical appointment no-show dataset is imbalanced,
and when classification algorithms are applied directly to the dataset, it is biased towards the
majority class, ignoring the minority class. To avoid this issue, multiple sampling techniques
such as Random Over Sampling (ROS), Random Under Sampling (RUS), Synthetic Minority
Oversampling TEchnique (SMOTE), ADAptive SYNthetic Sampling (ADASYN), Edited
Nearest Neighbor (ENN), and Condensed Nearest Neighbor (CNN) are applied in order to
make the dataset balanced. The performance is assessed by the Decision Tree classifier with
the listed sampling techniques and the best performance is identified.
Findings: This study focuses on the comparison of the performance metrics of various
sampling methods widely used. It is revealed that, compared to other techniques, the Recall
is high when ENN is applied CNN and ADASYN have performed equally well on the
Imbalanced data.
Research limitations: The testing was carried out with limited dataset and needs to be tested
with a larger dataset.
Practical implications: This framework will be useful whenever the data is imbalanced in
real world scenarios, which ultimately improves the performance.
Originality/value: This paper uses the rebalancing framework on medical appointment
no-show dataset to predict the no-shows and removes the bias towards minority class.