Estimating diabetes risk using Naïve Bayes classifiers

Juntong Li

doi:10.54254/2755-2721/21/20231113

Research Article

Open access

Published on 23 October 2023

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Li,J. (2023). Estimating diabetes risk using Naïve Bayes classifiers. Applied and Computational Engineering,21,28-35.

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Estimating diabetes risk using Naïve Bayes classifiers

Juntong Li *^,1,

¹ Northeastern University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/21/20231113

Abstract

Diabetes is now a common disease for modern people. Diabetes will cause some serious symptoms. Diabetes patients will face a painful life, high cure cost, and even death. So, it is necessary to correctly diagnose diabetes and analyze the factors that mainly cause diabetes to prevent the happen of diabetes. This essay mainly focuses on training the computer to help the doctor to train the computer. Three Naive Bayes classifications will be used to train the computer to do the prediction, including the Gaussian Naïve Bayes, Bernoulli Naïve Bayes, and Multinomial Naïve Bayes. To compare each method’s result, accuracy will be the main index to measure whether a method is good enough to put into use. Not only the accuracy, classification report, and confusion matrix also assist to measure the prediction. Finally, the Gaussian Naïve Bayes has the highest accuracy and when combined with the confusion matrix and the classification report, the Gaussian Naïve Bayes has a huge advantage over the other two models. The accuracy of these models still does not satisfy medical demand. Some deep learning and high-level model are expected to optimize this project.

Keywords

diabetes, machine learning, Naïve Bayes, classification

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References

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Cite this article

Li,J. (2023). Estimating diabetes risk using Naïve Bayes classifiers. Applied and Computational Engineering,21,28-35.

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 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-033-2(Print) / 978-1-83558-034-9(Online)

Conference date: 14 July 2023

Editor：Roman Bauer, Alan Wang, Marwan Omar

Series: Applied and Computational Engineering

Volume number: Vol.21

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

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