research-article

K-means Clustering Based Undersampling for Lower Back Pain Data

Authors:

Qian Zhou,

Bo Sun,

Yunsheng Song,

Shuang LiAuthors Info & Claims

ICBDT '20: Proceedings of the 3rd International Conference on Big Data Technologies

Pages 53 - 57

https://doi.org/10.1145/3422713.3422725

Published: 23 October 2020 Publication History

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Abstract

Many people are usually suffered from low back pain(LBP). It is very important to identify the LBP in the early stage. The classification algorithm in machine learning can help us to predict whether a person is suffered from low back pain, but class imbalance is often a problem in various real-world datasets including the LBP dataset. In this paper, LBP diagnosis based on a k-means clustering combined with undersampling has been proposed. The first strategy is to combine k-means and stratified random sampling to undersample(KSS). The second strategy is to combine k-means and Manhattan distance to undersample(KMD). Experiments have been conducted on LBP dataset by classification systems. The performance of the method is evaluated using the area under curve(AUC) metric. The results show that the highest classification accuracy (0.92) is obtained for the KSS is combined with logistic regression on LBP dataset. The KSS combine with linear SVM has higher accuracy and stability.

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Xu JNi JKe Y(2024)A Class-Aware Representation Refinement Framework for Graph ClassificationInformation Sciences10.1016/j.ins.2024.121061(121061)Online publication date: Jun-2024
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Kathiravan PShanmugavadivu PSaranya R(2023)Mitigating Imbalanced Data in Online Social Networks using Stratified K-Means Sampling2023 8th International Conference on Business and Industrial Research (ICBIR)10.1109/ICBIR57571.2023.10147677(883-888)Online publication date: 18-May-2023
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Index Terms

K-means Clustering Based Undersampling for Lower Back Pain Data
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory

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Published In

ICBDT '20: Proceedings of the 3rd International Conference on Big Data Technologies

September 2020

250 pages

ISBN:9781450387859

DOI:10.1145/3422713

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2020

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Funding Sources

Natural Science Foundation of Shandong Province
Major Scientific and Technological Innovation Project of Shandong Province

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ICBDT 2020

ICBDT 2020: 2020 3rd International Conference on Big Data Technologies

September 18 - 20, 2020

Qingdao, China

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Cited By

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Fofanah ALeigh A(2025)EATSA-GNN: Edge-Aware and Two-Stage attention for enhancing graph neural networks based on teacher–student mechanisms for graph node classificationNeurocomputing10.1016/j.neucom.2024.128686612(128686)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2024.128686
Xu JNi JKe Y(2024)A Class-Aware Representation Refinement Framework for Graph ClassificationInformation Sciences10.1016/j.ins.2024.121061(121061)Online publication date: Jun-2024
https://doi.org/10.1016/j.ins.2024.121061
Kathiravan PShanmugavadivu PSaranya R(2023)Mitigating Imbalanced Data in Online Social Networks using Stratified K-Means Sampling2023 8th International Conference on Business and Industrial Research (ICBIR)10.1109/ICBIR57571.2023.10147677(883-888)Online publication date: 18-May-2023
https://doi.org/10.1109/ICBIR57571.2023.10147677
Werner de Vargas VSchneider Aranda Jdos Santos Costa Rda Silva Pereira PVictória Barbosa J(2022)Imbalanced data preprocessing techniques for machine learning: a systematic mapping studyKnowledge and Information Systems10.1007/s10115-022-01772-865:1(31-57)Online publication date: 9-Nov-2022
https://doi.org/10.1007/s10115-022-01772-8
Shi SQiao KYang SWang LChen JYan B(2021)Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node ClassificationFrontiers in Neurorobotics10.3389/fnbot.2021.77568815Online publication date: 25-Nov-2021
https://doi.org/10.3389/fnbot.2021.775688

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