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An Ensemble Model for Diabetes Diagnosis in Large-scale and Imbalanced Dataset

Authors:

Shaoyin ChengAuthors Info & Claims

CF'17: Proceedings of the Computing Frontiers Conference

Pages 71 - 78

https://doi.org/10.1145/3075564.3075576

Published: 15 May 2017 Publication History

Abstract

Diabetes is becoming a more and more serious health challenge worldwide with the yearly rising prevalence, especially in developing countries. The vast majority of diabetes are type 2 diabetes, which has been indicated that about 80% of type 2 diabetes complications can be prevented or delayed by timely detection. In this paper, we propose an ensemble model to precisely diagnose the diabetic on a large-scale and imbalance dataset. The dataset used in our work covers millions of people from one province in China from 2009 to 2015, which is highly skew. Results on the real-world dataset prove that our method is promising for diabetes diagnosis with a high sensitivity, F3 and G --- mean, i.e, 91.00%, 58.24%, 86.69%, respectively.

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Cited By

Salmi MAtif DOliva DAbraham AVentura S(2024)Handling imbalanced medical datasets: review of a decade of researchArtificial Intelligence Review10.1007/s10462-024-10884-257:10Online publication date: 2-Sep-2024
https://doi.org/10.1007/s10462-024-10884-2
Anirudh GTalukdar U(2023)Automated Detection of Type 2 Diabetes with Imbalanced and Machine Learning MethodsMachine Learning, Image Processing, Network Security and Data Sciences10.1007/978-981-19-5868-7_3(29-40)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-981-19-5868-7_3
Guan HZhang C(2022)Predicting diabetes in imbalanced datasets using neural networksProceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3535508.3545540(1-10)Online publication date: 7-Aug-2022
https://dl.acm.org/doi/10.1145/3535508.3545540
Show More Cited By

Index Terms

An Ensemble Model for Diabetes Diagnosis in Large-scale and Imbalanced Dataset
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning algorithms
      1. Ensemble methods

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Information & Contributors

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Published In

cover image ACM Conferences

CF'17: Proceedings of the Computing Frontiers Conference

May 2017

450 pages

ISBN:9781450344876

DOI:10.1145/3075564

General Chair:
Roberto Giorgi
University of Siena, IT
,
Program Chairs:
Michela Becchi
North Carolina State University
,
Francesca Palumbo
University of Sassari, IT

Copyright © 2017 ACM.

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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SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 May 2017

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CF '17

Sponsor:

SIGMICRO

CF '17: Computing Frontiers Conference

May 15 - 17, 2017

Siena, Italy

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CF'17 Paper Acceptance Rate 43 of 87 submissions, 49%;

Overall Acceptance Rate 273 of 785 submissions, 35%

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CF '25

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22nd ACM International Conference on Computing Frontiers

May 28 - 30, 2025

Cagliari , Italy

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Cited By

Salmi MAtif DOliva DAbraham AVentura S(2024)Handling imbalanced medical datasets: review of a decade of researchArtificial Intelligence Review10.1007/s10462-024-10884-257:10Online publication date: 2-Sep-2024
https://doi.org/10.1007/s10462-024-10884-2
Anirudh GTalukdar U(2023)Automated Detection of Type 2 Diabetes with Imbalanced and Machine Learning MethodsMachine Learning, Image Processing, Network Security and Data Sciences10.1007/978-981-19-5868-7_3(29-40)Online publication date: 1-Jan-2023
https://doi.org/10.1007/978-981-19-5868-7_3
Guan HZhang C(2022)Predicting diabetes in imbalanced datasets using neural networksProceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3535508.3545540(1-10)Online publication date: 7-Aug-2022
https://dl.acm.org/doi/10.1145/3535508.3545540
Su CChiu SJang JLai F(2022)Improved inpatient deterioration detection in general wards by using time-series vital signsScientific Reports10.1038/s41598-022-16195-212:1Online publication date: 13-Jul-2022
https://doi.org/10.1038/s41598-022-16195-2
Premalatha GBai V(2022)Design and implementation of intelligent patient in-house monitoring system based on efficient XGBoost-CNN approachCognitive Neurodynamics10.1007/s11571-021-09754-216:5(1135-1149)Online publication date: 12-Jan-2022
https://doi.org/10.1007/s11571-021-09754-2
Wu CChu TJang J(2021)Current-Visit and Next-Visit Prediction for Fatty Liver Disease With a Large-Scale Dataset: Model Development and Performance ComparisonJMIR Medical Informatics10.2196/263989:8(e26398)Online publication date: 12-Aug-2021
https://doi.org/10.2196/26398
Jaiswal SGupta P(2021)MLP-DTP: Performance Evaluation of Diabetes Class Prediction2021 10th International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON)10.1109/IEMECON53809.2021.9689183(1-6)Online publication date: 1-Dec-2021
https://doi.org/10.1109/IEMECON53809.2021.9689183
Chang YHsing Y(2021)Emotion-infused deep neural network for emotionally resonant conversationApplied Soft Computing10.1016/j.asoc.2021.107861(107861)Online publication date: Sep-2021
https://doi.org/10.1016/j.asoc.2021.107861
Sekaran KSudha M(2020)Predicting autism spectrum disorder from associative genetic markers of phenotypic groups using machine learningJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-020-02155-z12:3(3257-3270)Online publication date: 31-May-2020
https://doi.org/10.1007/s12652-020-02155-z
Stazio AVictores JEstevez DBalaguer C(2019)A Study on Machine Vision Techniques for the Inspection of Health Personnels’ Protective Suits for the Treatment of Patients in Extreme IsolationElectronics10.3390/electronics80707438:7(743)Online publication date: 30-Jun-2019
https://doi.org/10.3390/electronics8070743
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