Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)

<Previous Article In Volume
Next Article In Volume>

A Comparative Study on Machine Learning Based Type 2 Diabetes Mellitus Prediction

Authors
Weiyi Zhan1, *
1School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, China
*Corresponding author. Email: wcx@bbc.edu.cn
Corresponding Author
Weiyi Zhan
Available Online 30 December 2022.
DOI
10.2991/978-94-6463-108-1_95How to use a DOI?
Keywords
Hybrid Random Forests Linear Model (HRFLM); type 2 diabetes mellitus(T2DM); Optuna; Exploratory Data Analysis (EDA)
Abstract

As is well known, one of the biggest health issues of our time is type 2 diabetes mellitus (T2DM). And it has been predicted that by 2045, its prevalence will have increased by more than 50% worldwide. Machine learning has emerged as a promising option for the prediction of diabetes after years of study in computational diagnosis of diabetes. However, the accuracy rate to date suggests that there is still a room for improvement. Using the PIMA Indian dataset, four machine learning methods (Hybrid Random Forests, Random Forests, XGBoost, and LGBM Classifiers) are examined for diabetes diagnosis and prediction. The data are prepared using Exploratory Data Analysis (EDA) and data standardization. The author also carried out auto-parameter tweaking using the optuna machine learning model. This project aims to create a data science model to Predict T2DM. For each of the methods proposed, the accuracy level is calculated as a percentage, and it is shown that Hybrid Random Forests with a Linear Model generate the highest degree of accuracy (86.4%).

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)
Series
Advances in Computer Science Research
Publication Date
30 December 2022
ISBN
978-94-6463-108-1
ISSN
2352-538X
DOI
10.2991/978-94-6463-108-1_95How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Weiyi Zhan
PY  - 2022
DA  - 2022/12/30
TI  - A Comparative Study on Machine Learning Based Type 2 Diabetes Mellitus Prediction
BT  - Proceedings of the 2022 International Conference on Computer Science, Information Engineering and Digital Economy (CSIEDE 2022)
PB  - Atlantis Press
SP  - 859
EP  - 871
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-108-1_95
DO  - 10.2991/978-94-6463-108-1_95
ID  - Zhan2022
ER  -