research-article

Feature Selection and Prediction Model for Type 2 Diabetes in the Chinese Population with Machine Learning

Authors:

Yongsheng Sang,

Li LuAuthors Info & Claims

CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering

Article No.: 103, Pages 1 - 7

https://doi.org/10.1145/3424978.3425085

Published: 20 October 2020 Publication History

Abstract

Diabetes is a chronic disease characterized by hyperglycemia. Based on the rising incidence of the disease in recent years, diabetes is affecting more and more families. In 2017 alone, it caused 5 million deaths and cost $850 billion in global healthcare. In this paper, we proposed a method to predict the prevalence of diabetes based on a selected set of features from physical examination data. We used Fisher's score, RFE and decision tree to select features. Random forest, logistic regression, SVM and MLP were used to predict the prevalence of diabetes. EA and Fisher' s score helped us to reduce dimensions. We used random forest to classify diabetes accurately. Our results show that the highest accuracy (0.987) can be achieved by using random forest with 85 features. The prediction accuracy using Fisher's Score with 19 features also reached 0.986. We finally selected 5 features based on our method to form a new dataset for diabetes prediction. The 5 features are fasting plasma glucose, HbA1c, HDL, total cholesterol level and hypertension. The values of accuracy, precision, sensitivity, F1 score, MCC and AUC were 0.977, 0.968, 0.812, 0.883, 0.875, and 0.905, respectively. Results show that our method can be successfully used to select features for diabetes classifier and improve its performance, which will provide support for clinicians to quickly identify diabetes.

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Cited By

Sreehari EBabu L(2024)Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection StrategyApplied Artificial Intelligence10.1080/08839514.2024.233191938:1Online publication date: Apr-2024
https://doi.org/10.1080/08839514.2024.2331919
Tuppad ADevi Patil S(2024)An efficient classification framework for Type 2 Diabetes incorporating feature interactionsExpert Systems with Applications10.1016/j.eswa.2023.122138239(122138)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122138
Katiyar NThakur HGhatak ARaj M(2024)Type-2 Diabetes Mellitus Prediction Through Ensemble Learning Technique Based on Gene Data and Machine Learning ApproachICT for Intelligent Systems10.1007/978-981-97-6675-8_47(565-576)Online publication date: 29-Oct-2024
https://doi.org/10.1007/978-981-97-6675-8_47
Show More Cited By

Index Terms

Feature Selection and Prediction Model for Type 2 Diabetes in the Chinese Population with Machine Learning
1. Applied computing
  1. Life and medical sciences
    1. Health informatics

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cover image ACM Other conferences

CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application Engineering

October 2020

1038 pages

ISBN:9781450377720

DOI:10.1145/3424978

Conference Chair:
Ali Emrouznejad,
Program Chair:
Jui-Sheng Rayson Chou

Copyright © 2020 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 the author(s) 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: 20 October 2020

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Funding Sources

National Natural Science Foundation of China
Department of Science and Technology of Sichuan Province
National Key Research and Development Program of China

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CSAE 2020

CSAE 2020: The 4th International Conference on Computer Science and Application Engineering

October 20 - 22, 2020

Sanya, China

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CSAE '20 Paper Acceptance Rate 179 of 387 submissions, 46%;

Overall Acceptance Rate 368 of 770 submissions, 48%

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Cited By

Sreehari EBabu L(2024)Critical Factor Analysis for prediction of Diabetes Mellitus using an Inclusive Feature Selection StrategyApplied Artificial Intelligence10.1080/08839514.2024.233191938:1Online publication date: Apr-2024
https://doi.org/10.1080/08839514.2024.2331919
Tuppad ADevi Patil S(2024)An efficient classification framework for Type 2 Diabetes incorporating feature interactionsExpert Systems with Applications10.1016/j.eswa.2023.122138239(122138)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122138
Katiyar NThakur HGhatak ARaj M(2024)Type-2 Diabetes Mellitus Prediction Through Ensemble Learning Technique Based on Gene Data and Machine Learning ApproachICT for Intelligent Systems10.1007/978-981-97-6675-8_47(565-576)Online publication date: 29-Oct-2024
https://doi.org/10.1007/978-981-97-6675-8_47
Middha KMittal A(2023)An effective feature selection method for type 2 diabetes mellitus detection using gene expression dataIntelligent Decision Technologies10.3233/IDT-22007717:3(595-606)Online publication date: 31-Jul-2023
https://doi.org/10.3233/IDT-220077
Alhalaseh RAL-Mashhadany DAbbadi M(2023)The Effect of Feature Selection on Diabetes Prediction Using Machine Learning2023 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC58397.2023.10218243(1-7)Online publication date: 9-Jul-2023
https://doi.org/10.1109/ISCC58397.2023.10218243
Ramya SKalaivani D(2023)Analysis of Machine Learning and Deep Learning for Diabetes Diagnosis in Design Thinking2023 International Conference on Energy, Materials and Communication Engineering (ICEMCE)10.1109/ICEMCE57940.2023.10434016(1-7)Online publication date: 14-Dec-2023
https://doi.org/10.1109/ICEMCE57940.2023.10434016
Wee BSivakumar SLim KWong WJuwono F(2023)Diabetes detection based on machine learning and deep learning approachesMultimedia Tools and Applications10.1007/s11042-023-16407-583:8(24153-24185)Online publication date: 10-Aug-2023
https://doi.org/10.1007/s11042-023-16407-5
Dhar JAyele N(2021)Multi-Tier Ensemble Learning Model With Neighborhood Component Analysis to Predict Health DiseasesIEEE Access10.1109/ACCESS.2021.31179639(138677-138715)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3117963

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