Research on using AdaBoost with K-Means and SMOTE to predict the incidence of diabetes

Hongquan Gao; Dan Zuo

doi:10.54254/2755-2721/6/20230749

Research Article

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Published on 14 June 2023

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Gao,H.;Zuo,D. (2023). Research on using AdaBoost with K-Means and SMOTE to predict the incidence of diabetes. Applied and Computational Engineering,6,67-73.

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Research on using AdaBoost with K-Means and SMOTE to predict the incidence of diabetes

Hongquan Gao *^,1, Dan Zuo ²

¹ Faculty of Engineering, the University of Hong Kong, Guangzhou, 511300, China
² College of Business, City University of Hong Kong, Guangzhou, 511300, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/6/20230749

Abstract

Diabetes is one of the most diseases in the world. In the last 40 years, the number of persons worldwide with diabetes has tripled. There were 108 million patients over the age of 18 in 1980 and 422 million in 2014, accounting for 8.5% of the entire population at that time. Diabetes directly caused 1.5 million fatalities worldwide in 2012, with hyperglycemia-related illnesses accounting for 2.2 million deaths. Diabetes is expected to be the 7th greatest cause of death by 2030 according to the World Health Organization. As the risk of diabetes increases, machine learning algorithms are used to improve early diagnosis of diabetes, and various researchers have also done some corresponding algorithms for predicting diabetes machine learning. As a commonly used machine learning algorithm, AdaBoost integrated learning algorithm is superior in the diagnosis and prediction of diabetes mellitus. In this paper, it is proposed that a hybrid model to detect the risk of diabetes. This hybrid model is detected and eliminated by K-means-based outliers, synthesizing the distribution of minority data oversampling techniques (SMOTE), and Adaboost to classify diabetes. According to the final experimental result, the model prediction accuracy is 0.950 after using the hybrid model in the PIMA dataset. In the future, if a larger number of sample training data are utilized for training, the model's accuracy will improve.

Keywords

classification, K-Means, SMOTE, Adaboost.

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References

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Cite this article

Gao,H.;Zuo,D. (2023). Research on using AdaBoost with K-Means and SMOTE to predict the incidence of diabetes. Applied and Computational Engineering,6,67-73.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Conference website: http://www.confspml.org

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)

Conference date: 25 February 2023

Editor：Omer Burak Istanbullu

Series: Applied and Computational Engineering

Volume number: Vol.6

ISSN：2755-2721(Print) / 2755-273X(Online)

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