Proceedings of International Conference on Neuroscience and Learning Technology (ICONSATIN 2025)

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Application of the Random Forest Method for Classifying Diabetes

Authors
Eka Putri Yuniarsih1, Mutia Nur Estri1, *, Najmah Istikaanah1, Idha Sihwaningrum1
1Jenderal Soedirman University, Grendeng, Indonesia
*Corresponding author. Email: mutia.estri@unsoed.ac.id
Corresponding Author
Mutia Nur Estri
Available Online 31 December 2025.
DOI
10.2991/978-2-38476-525-6_38How to use a DOI?
Keywords
Random Forest; Diabetes; Classification
Abstract

This study aims to apply the Random Forest method in classifying diabetes. The application was conducted using three train–test data split ratios, namely 80:20, 75:25, and 70:30. The classification model was based on the optimal combination of the max features parameters, n estimators, max depth, min samples split, and criterion. The classification results for both negative and positive diabetes patients demonstrate excellent performance. The model with an 80:20 data split ratio achieved an accuracy of 90.70%, the 75:25 model achieved an accuracy of 90.11%, and the 70:30 model achieved an accuracy of 89.71%. For new datasets, all three models produced nearly identical predictions, with over 99% similarity among models. Since the Random Forest method yields high and stable accuracy across different data split variations, it is considered suitable for classifying dibetes.

Copyright
© 2025 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.

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Volume Title
Proceedings of International Conference on Neuroscience and Learning Technology (ICONSATIN 2025)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
31 December 2025
ISBN
978-2-38476-525-6
ISSN
2352-5398
DOI
10.2991/978-2-38476-525-6_38How to use a DOI?
Copyright
© 2025 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  - Eka Putri Yuniarsih
AU  - Mutia Nur Estri
AU  - Najmah Istikaanah
AU  - Idha Sihwaningrum
PY  - 2025
DA  - 2025/12/31
TI  - Application of the Random Forest Method for Classifying Diabetes
BT  - Proceedings of International Conference on Neuroscience and Learning Technology (ICONSATIN 2025)
PB  - Atlantis Press
SP  - 376
EP  - 387
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-525-6_38
DO  - 10.2991/978-2-38476-525-6_38
ID  - Yuniarsih2025
ER  -