<abstract xmlns="http://www.w3.org/1999/xhtml"><sec><h3>Purpose</h3><p>This paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine Learning.</p></sec><sec><h3>Design/methodology/approach</h3><p>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.</p></sec><sec><h3>Findings</h3><p>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.</p></sec><sec><h3>Research limitations</h3><p>The testing was carried out with limited dataset and needs to be tested with a larger dataset.</p></sec><sec><h3>Practical implications</h3><p>This framework will be useful whenever the data is imbalanced in real world scenarios, which ultimately improves the performance.</p></sec><sec><h3>Originality/value</h3><p>This paper uses the rebalancing framework on medical appointment no-show dataset to predict the no-shows and removes the bias towards minority class.</p></sec></abstract>

PurposeThis paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine Learning.Design/methodology/approachThe 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.FindingsThis 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 limitationsThe testing was carried out with limited dataset and needs to be tested with a larger dataset.Practical implicationsThis framework will be useful whenever the data is imbalanced in real world scenarios, which ultimately improves the performance.Originality/valueThis paper uses the rebalancing framework on medical appointment no-show dataset to predict the no-shows and removes the bias towards minority class.

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

Department of Computer Science and Engineering

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

PurposeThis paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine...

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

Article Category: Research Paper

Pubblicato online: 27 gen 2021

Pagine: 178 - 192

Ricevuto: 29 apr 2020

Accettato: 21 dic 2020

DOI: https://doi.org/10.2478/jdis-2021-0011

Parole chiave
Imbalanced data, Sampling methods, Machine learning, Classification

© 2021 Ulagapriya Krishnan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Article Category: Research Paper

Pubblicato online: 27 gen 2021

Pagine: 178 - 192

Ricevuto: 29 apr 2020

Accettato: 21 dic 2020

DOI: https://doi.org/10.2478/jdis-2021-0011

Parole chiaveImbalanced data, Sampling methods, Machine learning, Classification

© 2021 Ulagapriya Krishnan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Imbalanced data, Sampling methods, Machine learning, Classification