Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost)

Authors

  • Kartina Diah Kusuma Wardhani Teknik Informatika, Politeknik Caltex Riau, Indonesia
  • Memen Akbar Teknik Rekayasa Komputer, Politeknik Caltex Riau, Indonesia

DOI:

https://doi.org/10.15575/join.v7i2.970

Keywords:

Diabetes, Prediction, Machine Learning, XGBoost

Abstract

One of the uses of medical data from diabetes patients is to produce models that can be used by medical personnel to predict and identify diabetes in patients. Various techniques are used to be able to provide a diabetes model as early as possible based on the symptoms experienced by diabetic patients, including using machine learning. The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost). XGBoost is an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations. Errors made by the previous model are tried to be corrected by the next model by adding some weight to the model. The diabetes prediction model using XGBoost is shown in the form of a tree, with the accuracy of the model produced in this study of 98.71%

References

V. Vaidya and L. K. Vishwamitra Scholar, “Diabetes Detection using Convolutional Neural Network through Feature Sequencing,†Turkish J. Comput. Math. Educ., vol. 12, no. 10, pp. 2783–2789, 2021.

A. D. Association, “Classification and diagnosis of diabetes,†Diabetes Care, vol. 38 Suppl:S, 2015, doi: 10.2337/dc15-S005.

S. Patel, R. Patel, N. Ganatra, and A. Patel, “Predicting a Risk of Diabetes at Early Stage using Machine Learning Approach,†Turkish J. Comput. Math. Educ., vol. 12, no. 10, pp. 5277–5284, 2021.

D. Elias and T. Maria, “Data-Driven Machine-Learning Methods for Diabetes Risk Prediction,†Sensors, p. 5304, 2022, doi: https://doi.org/10.3390/s22145304.

M. M. F. Islam, R. Ferdousi, S. Rahman, and H. Y. Bushra, “Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques,†Comput. Vis. Mach. Intell. Med. Image Anal. Springer, Singapore., pp. 113-125., 2020.

U. e. Laila, K. Mahboob, A. W. Khan, F. Khan, and W. Taekeun, “An Ensemble Approach to Predict Early-Stage Diabetes Risk Using Machine Learning: An Empirical Study,†Sensors, vol. 22, no. 14, pp. 1–15, 2022, doi: 10.3390/s22145247.

T. Dendup, X. Feng, S. Clingan, and T. Astell-Burt, “Environmental risk factors for developing type 2 diabetes mellitus: A systematic review,†Int. J. Environ. Res. Public Health, vol. 15, no. 1, 2018, doi: 10.3390/ijerph15010078.

Y. Tan, H. Chen, J. Zhang, R. Tang, and P. Liu, “Early Risk Prediction of Diabetes Based on GA-Stacking,†Appl. Sci., vol. 12, no. 2, 2022, doi: 10.3390/app12020632.

S. K. Bhoi et al., “Prediction of Diabetes in Females of Pima Indian Heritage: A Complete Supervised Learning Approach,†Turkish J. Comput. Math. Educ., vol. 12, no. 10, pp. 3074–3084, 2021.

V. R. Geetha, N. Jayaveeran, and A. S. A. K. N, “Classification Of Gestational Diabetes Using Modified Fuzzy C Means Clustering And Machine Learning Technique,†vol. 12, no. 10, pp. 2416–2427, 2021.

R. Saxena and S. Kumar Sharma Manali Gupta, “Role of K-nearest neighbour in detection of Diabetes Mellitus,†Turkish J. Comput. Math. Educ., vol. 12, no. 10, pp. 373–376, 2021.

J. J. S. M. Et. al., “Predictive Modeling Framework for Diabetes Classification Using Big Data Tools and Machine Learning,†Turkish J. Comput. Math. Educ., vol. 12, no. 10, pp. 818–823, 2021, doi: 10.17762/turcomat.v12i10.4255.

N. N. Pandika Pinata, I. M. Sukarsa, and N. K. Dwi Rusjayanthi, “Prediksi Kecelakaan Lalu Lintas di Bali dengan XGBoost pada Python,†J. Ilm. Merpati (Menara Penelit. Akad. Teknol. Informasi), vol. 8, no. 3, p. 188, 2020, doi: 10.24843/jim.2020.v08.i03.p04.

H. Mo, H. Sun, J. Liu, and S. Wei, “Developing window behavior models for residential buildings using XGBoost algorithm,†Energy Build., vol. 205, pp. 1–23, 2019, doi: 10.1016/j.enbuild.2019.109564.

A. Mello, “XGBoost: theory and practice,†https://towardsdatascience.com/xgboost-theory-and-practice-fb8912930ad6, 2020.

Downloads

Published

2022-12-29

Issue

Vol. 7 No. 2 (2022)

Section

Article

Citation Check

License

You are free to:

 

Under the following terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

  • NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.

  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

 

Notices:

  • You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
  • No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.